❓ چرا سرویس گذر از تحریم F14
📌 دارای تیم فنی قوی و متخصص و نوآور
📌 ارائه بیش از 60 سرور از 15 کشور برای هر اشتراک (در حال افزایش🔼)
📌 ارائه سرویس موقت جایگزین در مواقع بحران برای حفظ ارتباط کاربران
📌 دارای پشتیبانی با دانش بالا و صبر و حوصله
📌 ارائه آموزشهای لازم بصورت تصویری برای کاربران
📌 ارائه سرویس برای تمامی سیستم عاملها با یک اشتراک
📌 ارائه سرویس برای تمامی ISPها
📌 ارائه کانکشنهای سازگار با هر ISP
📌 ارائه سرورهای VIP برای اینترنتهای دارای محدودیت بسیار بالا
📌 نمایش ریز مصرف کاربران
📌 امکان سفارش و تمدید بصورت کاملا خودکار در ۲۴ ساعت شبانه روز
📌 امکان سفارش و تمدید با رمز ارزها
☄️ اینها بخشی از ویژگیهای سرویس ما میباشد.
✔️ از نــظـر مـا فــروش پـایـان کــار نـیـسـت بـلـکه آغــاز یــک تـعـهد مـیبـاشـد.
🔸 https://news.1rj.ru/str/F14PanelBot
پ.ن: پیشنهاد ویژه من به شما کیفیت پاسخگویی و پشتیبانی عالی برای کانکشنها
📌 دارای تیم فنی قوی و متخصص و نوآور
📌 ارائه بیش از 60 سرور از 15 کشور برای هر اشتراک (در حال افزایش🔼)
📌 ارائه سرویس موقت جایگزین در مواقع بحران برای حفظ ارتباط کاربران
📌 دارای پشتیبانی با دانش بالا و صبر و حوصله
📌 ارائه آموزشهای لازم بصورت تصویری برای کاربران
📌 ارائه سرویس برای تمامی سیستم عاملها با یک اشتراک
📌 ارائه سرویس برای تمامی ISPها
📌 ارائه کانکشنهای سازگار با هر ISP
📌 ارائه سرورهای VIP برای اینترنتهای دارای محدودیت بسیار بالا
📌 نمایش ریز مصرف کاربران
📌 امکان سفارش و تمدید بصورت کاملا خودکار در ۲۴ ساعت شبانه روز
📌 امکان سفارش و تمدید با رمز ارزها
☄️ اینها بخشی از ویژگیهای سرویس ما میباشد.
✔️ از نــظـر مـا فــروش پـایـان کــار نـیـسـت بـلـکه آغــاز یــک تـعـهد مـیبـاشـد.
🔸 https://news.1rj.ru/str/F14PanelBot
پ.ن: پیشنهاد ویژه من به شما کیفیت پاسخگویی و پشتیبانی عالی برای کانکشنها
👍1
ده #ایده_جذاب که در یک ماه گذشته منتشر شد. قسمت ۱ از ۳
1) LLM explains neurons in LLMs - applies GPT-4 to automatically write explanations on the behavior of neurons in LLMs and even score those explanations; this offers a promising way to improve interpretability in future LLMs and potentially detect alignment and safety problems.
2) PaLM 2 - a new state-of-the-art language model integrated into AI features and tools like Bard and the PaLM API; displays competitive performance in mathematical reasoning compared to GPT-4; instruction-tuned model, Flan-PaLM 2, shows good performance on benchmarks like MMLU and BIG-bench Hard.
3) ImageBind - an approach that learns joint embedding data across six modalities at once; extends zero-shot capabilities to new modalities and enables emergent applications including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection, and generation.
4) TidyBot - shows that robots can combine language-based planning and perception with the few-shot summarization capabilities of LLMs to infer generalized user preferences that are applicable to future interactions.
5. Unfaithful Explanations in Chain-of-Thought Prompting - demonstrates that CoT explanations can misrepresent the true reason for a model’s prediction; when models are biased towards incorrect answers, CoT generation explanations supporting those answers.
6) InstructBLIP - explores visual-language instruction tuning based on the pre-trained BLIP-2 models; achieves state-of-the-art zero-shot performance on 13 held-out datasets, outperforming BLIP-2 and Flamingo.
7) Active Retrieval Augmented LLMs - introduces FLARE, retrieval augmented generation to improve the reliability of LLMs; FLARE actively decides when and what to retrieve across the course of the generation; demonstrates superior or competitive performance on long-form knowledge-intensive generation tasks.
8) FrugalGPT - presents strategies to reduce the inference cost associated with using LLMs while improving performance.
9) StarCoder - an open-access 15.5B parameter LLM with 8K context length and is trained on large amounts of code spanning 80+ programming languages.
10) MultiModal-GPT - a vision and language model for multi-round dialogue with humans; the model is fine-tuned from OpenFlamingo, with LoRA added in the cross-attention and self-attention parts of the language model.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
1) LLM explains neurons in LLMs - applies GPT-4 to automatically write explanations on the behavior of neurons in LLMs and even score those explanations; this offers a promising way to improve interpretability in future LLMs and potentially detect alignment and safety problems.
2) PaLM 2 - a new state-of-the-art language model integrated into AI features and tools like Bard and the PaLM API; displays competitive performance in mathematical reasoning compared to GPT-4; instruction-tuned model, Flan-PaLM 2, shows good performance on benchmarks like MMLU and BIG-bench Hard.
3) ImageBind - an approach that learns joint embedding data across six modalities at once; extends zero-shot capabilities to new modalities and enables emergent applications including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection, and generation.
4) TidyBot - shows that robots can combine language-based planning and perception with the few-shot summarization capabilities of LLMs to infer generalized user preferences that are applicable to future interactions.
5. Unfaithful Explanations in Chain-of-Thought Prompting - demonstrates that CoT explanations can misrepresent the true reason for a model’s prediction; when models are biased towards incorrect answers, CoT generation explanations supporting those answers.
6) InstructBLIP - explores visual-language instruction tuning based on the pre-trained BLIP-2 models; achieves state-of-the-art zero-shot performance on 13 held-out datasets, outperforming BLIP-2 and Flamingo.
7) Active Retrieval Augmented LLMs - introduces FLARE, retrieval augmented generation to improve the reliability of LLMs; FLARE actively decides when and what to retrieve across the course of the generation; demonstrates superior or competitive performance on long-form knowledge-intensive generation tasks.
8) FrugalGPT - presents strategies to reduce the inference cost associated with using LLMs while improving performance.
9) StarCoder - an open-access 15.5B parameter LLM with 8K context length and is trained on large amounts of code spanning 80+ programming languages.
10) MultiModal-GPT - a vision and language model for multi-round dialogue with humans; the model is fine-tuned from OpenFlamingo, with LoRA added in the cross-attention and self-attention parts of the language model.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍8❤2
۸۰۰ کلاس درس دانشگاهی، عموما از دانشگاه های Ivy League آمریکا و معتبر در زمینه Computer Science. فرصت خوبی برای یادگیری و یا آشنایی با نحوه تدریس و کلاس های دانشگاهی.
bit.ly/3472Iia
#منابع #منابع_پیشنهادی #فیلم #کلاس_آموزشی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
bit.ly/3472Iia
#منابع #منابع_پیشنهادی #فیلم #کلاس_آموزشی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
❤6
ده #ایده_جذاب که در یک ماه گذشته منتشر شد. قسمت ۲ از ۳
1) Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold - an approach for controlling GANs that allows dragging points of the image to precisely reach target points in a user-interactive manner.
2) Evidence of Meaning in Language Models Trained on Programs - argues that language models can learn meaning despite being trained only to perform next token prediction on text.
3) Towards Expert-Level Medical Question Answering with Large Language Models - a top-performing LLM for medical question answering; scored up to 86.5% on the MedQA dataset (a new state-of-the-art); approaches or exceeds SoTA across MedMCQA, PubMedQA, and MMLU clinical topics datasets.
4) MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers - a multi-scale decoder architecture enabling end-to-end modeling of sequences of over one million bytes; enables sub-quadratic self-attention and improved parallelism during decoding.
5. StructGPT: A General Framework for Large Language Model to Reason over Structured Data - improves the zero-shot reasoning ability of LLMs over structured data; effective for solving question answering tasks based on structured data.
6) TinyStories: How Small Can Language Models Be and Still Speak Coherent English? - uses a synthetic dataset of short stories to train and evaluate LMs that are much smaller than SoTA models but can produce fluent and consistent stories with several paragraphs, and demonstrate reasoning capabilities.
7) DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining - trains a small proxy model over domains to produce domain weights without knowledge of downstream tasks; it then resamples a dataset with the domain weights and trains a larger model; this enables using a 280M proxy model to train an 8B model (30x larger) more efficiently.
8) CodeT5+: Open Code Large Language Models for Code Understanding and Generation - supports a wide range of code understanding and generation tasks and different training methods to improve efficacy and computing efficiency; tested on 20 code-related benchmarks using different settings like zero-shot, fine-tuning, and instruction tuning; achieves SoTA on tasks like code completion, math programming, and text-to-code retrieval tasks.
9) Symbol tuning improves in-context learning in language models - an approach to finetune LMs on in-context input-label pairs where natural language labels are replaced by arbitrary symbols; boosts performance on unseen in-context learning tasks and algorithmic reasoning tasks.
10) Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability - shows that PaLM is exposed to over 30 million translation pairs across at least 44 languages; shows that incidental bilingualism connects to the translation capabilities of PaLM.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
1) Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold - an approach for controlling GANs that allows dragging points of the image to precisely reach target points in a user-interactive manner.
2) Evidence of Meaning in Language Models Trained on Programs - argues that language models can learn meaning despite being trained only to perform next token prediction on text.
3) Towards Expert-Level Medical Question Answering with Large Language Models - a top-performing LLM for medical question answering; scored up to 86.5% on the MedQA dataset (a new state-of-the-art); approaches or exceeds SoTA across MedMCQA, PubMedQA, and MMLU clinical topics datasets.
4) MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers - a multi-scale decoder architecture enabling end-to-end modeling of sequences of over one million bytes; enables sub-quadratic self-attention and improved parallelism during decoding.
5. StructGPT: A General Framework for Large Language Model to Reason over Structured Data - improves the zero-shot reasoning ability of LLMs over structured data; effective for solving question answering tasks based on structured data.
6) TinyStories: How Small Can Language Models Be and Still Speak Coherent English? - uses a synthetic dataset of short stories to train and evaluate LMs that are much smaller than SoTA models but can produce fluent and consistent stories with several paragraphs, and demonstrate reasoning capabilities.
7) DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining - trains a small proxy model over domains to produce domain weights without knowledge of downstream tasks; it then resamples a dataset with the domain weights and trains a larger model; this enables using a 280M proxy model to train an 8B model (30x larger) more efficiently.
8) CodeT5+: Open Code Large Language Models for Code Understanding and Generation - supports a wide range of code understanding and generation tasks and different training methods to improve efficacy and computing efficiency; tested on 20 code-related benchmarks using different settings like zero-shot, fine-tuning, and instruction tuning; achieves SoTA on tasks like code completion, math programming, and text-to-code retrieval tasks.
9) Symbol tuning improves in-context learning in language models - an approach to finetune LMs on in-context input-label pairs where natural language labels are replaced by arbitrary symbols; boosts performance on unseen in-context learning tasks and algorithmic reasoning tasks.
10) Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability - shows that PaLM is exposed to over 30 million translation pairs across at least 44 languages; shows that incidental bilingualism connects to the translation capabilities of PaLM.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍4
ده #ایده_جذاب که در یک ماه گذشته منتشر شد. قسمت 3 از 3
1) QLoRA - an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning performance.
2) LIMA - a new 65B parameter LLaMa model fine-tuned on 1000 carefully curated prompts and responses; it doesn't use RLHF, generalizes well to unseen tasks not available in the training data, and generates responses equivalent or preferred to GPT-4 in 43% of cases, and even higher compared to Bard.
3) Voyager - an LLM-powered embodied lifelong learning agent in Minecraft that can continuously explore worlds, acquire skills, and make novel discoveries without human intervention.
4) Gorilla - a finetuned LLaMA-based model that surpasses GPT-4 on writing API calls. This capability can help identify the right API, boosting the ability of LLMs to interact with external tools to complete specific tasks.
5. The False Promise of Imitating Proprietary LLMs - provides a critical analysis of models that are finetuned on the outputs of a stronger model; argues that model imitation is a false premise and that the higher leverage action to improve open source models is to develop better base models.
6) Sophia - presents a simple scalable second-order optimizer that has negligible average per-step time and memory overhead; on language modeling, Sophia achieves 2x speed-up compared to Adam in the number of steps, total compute, and wall-clock time.
7) The Larger They Are, the Harder They Fail - shows that LLMs fail to generate correct Python code when default function names are swapped; they also strongly prefer incorrect continuation as they become bigger.
8) Model Evaluation for Extreme Risks - discusses the importance of model evaluation for addressing extreme risks and making responsible decisions about model training, deployment, and security.
9) LLM Research Directions - discusses a list of research directions for students looking to do research with LLMs.
10) Reinventing RNNs for the Transformer Era - proposes an approach that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs; results show that the method performs on part with similarly sized Transformers.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
1) QLoRA - an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning performance.
2) LIMA - a new 65B parameter LLaMa model fine-tuned on 1000 carefully curated prompts and responses; it doesn't use RLHF, generalizes well to unseen tasks not available in the training data, and generates responses equivalent or preferred to GPT-4 in 43% of cases, and even higher compared to Bard.
3) Voyager - an LLM-powered embodied lifelong learning agent in Minecraft that can continuously explore worlds, acquire skills, and make novel discoveries without human intervention.
4) Gorilla - a finetuned LLaMA-based model that surpasses GPT-4 on writing API calls. This capability can help identify the right API, boosting the ability of LLMs to interact with external tools to complete specific tasks.
5. The False Promise of Imitating Proprietary LLMs - provides a critical analysis of models that are finetuned on the outputs of a stronger model; argues that model imitation is a false premise and that the higher leverage action to improve open source models is to develop better base models.
6) Sophia - presents a simple scalable second-order optimizer that has negligible average per-step time and memory overhead; on language modeling, Sophia achieves 2x speed-up compared to Adam in the number of steps, total compute, and wall-clock time.
7) The Larger They Are, the Harder They Fail - shows that LLMs fail to generate correct Python code when default function names are swapped; they also strongly prefer incorrect continuation as they become bigger.
8) Model Evaluation for Extreme Risks - discusses the importance of model evaluation for addressing extreme risks and making responsible decisions about model training, deployment, and security.
9) LLM Research Directions - discusses a list of research directions for students looking to do research with LLMs.
10) Reinventing RNNs for the Transformer Era - proposes an approach that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs; results show that the method performs on part with similarly sized Transformers.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍1
ده #ایده_جذاب که در هفته گذشته منتشر شد.
1) Let’s Verify Step by Step - achieves state-of-the-art mathematical problem solving by rewarding each correct step of reasoning in a chain-of-thought instead of rewarding the final answer; the model solves 78% of problems from a representative subset of the MATH test set.
2) No Positional Encodings - shows that explicit position embeddings are not essential for decoder-only Transformers; shows that other positional encoding methods like ALiBi and Rotary are not well suited for length generalization.
3) BiomedGPT - a unified biomedical generative pretrained transformer model for vision, language, and multimodal tasks. Achieves state-of-the-art performance across 5 distinct tasks with 20 public datasets spanning over 15 unique biomedical modalities.
4) Thought Cloning - introduces an imitation learning framework to learn to think while acting; the idea is not only to clone the behaviors of human demonstrators but also the thoughts humans have when performing behaviors.
5. Fine-Tuning Language Models with Just Forward Passes - proposes a memory-efficient zeroth-order optimizer and a corresponding SGD algorithm to finetune large LMs with the same memory footprint as inference.
6) MERT - an acoustic music understanding model with large-scale self-supervised training; it incorporates a superior combination of teacher models to outperform conventional speech and audio approaches.
7) Bytes Are All You Need - investigates performing classification directly on file bytes, without needing to decode files at inference time; achieves ImageNet Top-1 accuracy of 77.33% using a transformer backbone; achieves 95.42% accuracy when operating on WAV files from the Speech Commands v2 dataset.
8) Direct Preference Optimization - while helpful to train safe and useful LLMs, the RLHF process can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.
9) SQL-PaLM - an LLM-based Text-to-SQL adopted from PaLM-2; achieves SoTA in both in-context learning and fine-tuning settings; the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark; few-shot SQL-PaLM also outperforms few-shot GPT-4 by 9.9%, using a simple prompting approach.
10) CodeTF - an open-source Transformer library for state-of-the-art code LLMs; supports pretrained code LLMs and popular code benchmarks, including standard methods to train and serve code LLMs efficiently.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
1) Let’s Verify Step by Step - achieves state-of-the-art mathematical problem solving by rewarding each correct step of reasoning in a chain-of-thought instead of rewarding the final answer; the model solves 78% of problems from a representative subset of the MATH test set.
2) No Positional Encodings - shows that explicit position embeddings are not essential for decoder-only Transformers; shows that other positional encoding methods like ALiBi and Rotary are not well suited for length generalization.
3) BiomedGPT - a unified biomedical generative pretrained transformer model for vision, language, and multimodal tasks. Achieves state-of-the-art performance across 5 distinct tasks with 20 public datasets spanning over 15 unique biomedical modalities.
4) Thought Cloning - introduces an imitation learning framework to learn to think while acting; the idea is not only to clone the behaviors of human demonstrators but also the thoughts humans have when performing behaviors.
5. Fine-Tuning Language Models with Just Forward Passes - proposes a memory-efficient zeroth-order optimizer and a corresponding SGD algorithm to finetune large LMs with the same memory footprint as inference.
6) MERT - an acoustic music understanding model with large-scale self-supervised training; it incorporates a superior combination of teacher models to outperform conventional speech and audio approaches.
7) Bytes Are All You Need - investigates performing classification directly on file bytes, without needing to decode files at inference time; achieves ImageNet Top-1 accuracy of 77.33% using a transformer backbone; achieves 95.42% accuracy when operating on WAV files from the Speech Commands v2 dataset.
8) Direct Preference Optimization - while helpful to train safe and useful LLMs, the RLHF process can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.
9) SQL-PaLM - an LLM-based Text-to-SQL adopted from PaLM-2; achieves SoTA in both in-context learning and fine-tuning settings; the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark; few-shot SQL-PaLM also outperforms few-shot GPT-4 by 9.9%, using a simple prompting approach.
10) CodeTF - an open-source Transformer library for state-of-the-art code LLMs; supports pretrained code LLMs and popular code benchmarks, including standard methods to train and serve code LLMs efficiently.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍3
یک نقشه راهی برای یادگیری و ۹ دوره رایگان
Generative AI Learning Path
cloudskillsboost.google/paths/118
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Generative AI Learning Path
cloudskillsboost.google/paths/118
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
❤5
اگر راجب گرافها در مدلهای زبانی دنبال منابع و دیتاستهای خوبی میگشتید اینو پیشنهاد میدم.
Graph-Related Large Language Models (LLMs).
https://github.com/XiaoxinHe/Awesome-Graph-LLM
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Graph-Related Large Language Models (LLMs).
https://github.com/XiaoxinHe/Awesome-Graph-LLM
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
🔥4
دوره خوبی هست، خواستید یه سر بزنید و نگاه بکنید:
https://maktabkhooneh.org/course/%D8%A2%D9%85%D9%88%D8%B2%D8%B4-%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C-%D9%85%D8%A7%D8%B4%DB%8C%D9%86-%DA%A9%D8%A7%D8%B1%D8%A8%D8%B1%D8%AF%DB%8C-mk2450/
دکتر تهرانیپور عزیز تهیه کردند.
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
https://maktabkhooneh.org/course/%D8%A2%D9%85%D9%88%D8%B2%D8%B4-%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C-%D9%85%D8%A7%D8%B4%DB%8C%D9%86-%DA%A9%D8%A7%D8%B1%D8%A8%D8%B1%D8%AF%DB%8C-mk2450/
دکتر تهرانیپور عزیز تهیه کردند.
#هوش_مصنوعی #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
مکتبخونه
آموزش یادگیری ماشین با 10 پروژه کاربردی و نکات مهم کتابخانه ها
اگر به دنبال یادگیری ماشین لرنینگ به صورت کاربردی هستید و دنبال دوره ای هستید تا نکات بسیار مهم کتابخانههای کاربردی را به شما یاد دهد با ما در دوره یادگیری ماشین کاربردی همراه باشید تا با هم به حل 10 پروژه سنگین و خوب بپردازیم.
❤3
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دراین مقاله اومدن از مدل Segment Anything (SAM) استفاده کردن و یک ماژول سبک وزن Mask-to-Matte (M2M) را برای تطبیق عکسها و... استفاده کردند که به نظرم یک انقلابیه...!!
Matting everything (MAM)
https://huggingface.co/papers/2306.05399
پ.ن:در این مقاله به نظرم میشه صحبت دکتر عسگری رو تایید کرد که پردازش تصویر گیم اور شده پ!!
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Matting everything (MAM)
https://huggingface.co/papers/2306.05399
پ.ن:در این مقاله به نظرم میشه صحبت دکتر عسگری رو تایید کرد که پردازش تصویر گیم اور شده پ!!
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
❤7
نظرات دکتر علی شریفی زارچی استاد کامپیوتر دانشگاه شریف راجب مراحل یادگیری #هوش_مصنوعی
https://twitter.com/SharifiZarchi/status/1667131051104149505
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
https://twitter.com/SharifiZarchi/status/1667131051104149505
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍4👎2
Forwarded from Meysam
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ترک کردن همه چی همه جا در یک لحظه!
این مقاله خیلی خیلی خوبه حتما بخونید در مورد ترکینگ هستش:
https://arxiv.org/abs/2306.05422
(یادتون هست میگفتم پردازش تصویر گیم آور شد؟ بعد از مدل segment anything دیگه خیلی از تسکها ساده تر شدند)
این مقاله خیلی خیلی خوبه حتما بخونید در مورد ترکینگ هستش:
https://arxiv.org/abs/2306.05422
(یادتون هست میگفتم پردازش تصویر گیم آور شد؟ بعد از مدل segment anything دیگه خیلی از تسکها ساده تر شدند)
Meysam
ترک کردن همه چی همه جا در یک لحظه! این مقاله خیلی خیلی خوبه حتما بخونید در مورد ترکینگ هستش: https://arxiv.org/abs/2306.05422 (یادتون هست میگفتم پردازش تصویر گیم آور شد؟ بعد از مدل segment anything دیگه خیلی از تسکها ساده تر شدند)
در ادامه تکمیل این ایده از این مقاله و این مقاله هم اینو مطالعه کنید
Background Prompting for Improved Object Depth
https://huggingface.co/papers/2306.05428
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Background Prompting for Improved Object Depth
https://huggingface.co/papers/2306.05428
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍2
Transformers as Statisticians
Unveiling a new mechanism "In-Context Algorithm Selection" for In-Context Learning (ICL) in LLMs/transformers.
arxiv.org/abs/2306.04637
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Unveiling a new mechanism "In-Context Algorithm Selection" for In-Context Learning (ICL) in LLMs/transformers.
arxiv.org/abs/2306.04637
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
ده #ایده_جذاب که در هفته گذشته منتشر شد.
1) Tracking Everything Everywhere All at Once - propose a test-time optimization method for estimating dense and long-range motion; enables accurate, full-length motion estimation of every pixel in a video.
2) AlphaDev - a deep reinforcement learning agent which discovers faster sorting algorithms from scratch; the algorithms outperform previously known human benchmarks and have been integrated into the LLVM C++ library.
3) Sparse-Quantized Representation - a new compressed format and quantization technique that enables near-lossless compression of LLMs across model scales; “allows LLM inference at 4.75 bits with a 15% speedup”.
4) MusicGen - a simple and controllable model for music generation built on top of a single-stage transformer LM together with efficient token interleaving patterns; it can be conditioned on textual denoscriptions or melodic features and shows high performance on a standard text-to-music benchmark.
5. Augmenting LLMs with Databases - combines an LLM with a set of SQL databases, enabling a symbolic memory framework; completes tasks via LLM generating SQL instructions that manipulate the DB autonomously.
6) Concept Scrubbing in LLM - presents a method called LEAst-squares Concept Erasure (LEACE) to erase target concept information from every layer in a neural network; it’s used for reducing gender bias in BERT embeddings.
7) Fine-Grained RLHF - trains LMs with fine-grained human feedback; instead of using overall preference, more explicit feedback is provided at the segment level which helps to improve efficacy on long-form question answering, reduce toxicity, and enables LM customization.
8) Hierarchical Vision Transformer - pretrains vision transformers with a visual pretext task (MAE), while removing unnecessary components from a state-of-the-art multi-stage vision transformer; this enables a simple hierarchical vision transformer that’s more accurate and faster at inference and during training.
9) Humor in ChatGPT - explores ChatGPT’s capabilities to grasp and reproduce humor; finds that over 90% of 1008 generated jokes were the same 25 jokes and that ChatGPT is also overfitted to a particular joke structure.
10) Imitating Reasoning Process of Larger LLMs - develops a 13B parameter model that learns to imitate the reasoning process of large foundational models like GPT-4; it leverages large-scale and diverse imitation data and surpasses instruction-tuned models such as Vicuna-13B in zero-shot reasoning.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
1) Tracking Everything Everywhere All at Once - propose a test-time optimization method for estimating dense and long-range motion; enables accurate, full-length motion estimation of every pixel in a video.
2) AlphaDev - a deep reinforcement learning agent which discovers faster sorting algorithms from scratch; the algorithms outperform previously known human benchmarks and have been integrated into the LLVM C++ library.
3) Sparse-Quantized Representation - a new compressed format and quantization technique that enables near-lossless compression of LLMs across model scales; “allows LLM inference at 4.75 bits with a 15% speedup”.
4) MusicGen - a simple and controllable model for music generation built on top of a single-stage transformer LM together with efficient token interleaving patterns; it can be conditioned on textual denoscriptions or melodic features and shows high performance on a standard text-to-music benchmark.
5. Augmenting LLMs with Databases - combines an LLM with a set of SQL databases, enabling a symbolic memory framework; completes tasks via LLM generating SQL instructions that manipulate the DB autonomously.
6) Concept Scrubbing in LLM - presents a method called LEAst-squares Concept Erasure (LEACE) to erase target concept information from every layer in a neural network; it’s used for reducing gender bias in BERT embeddings.
7) Fine-Grained RLHF - trains LMs with fine-grained human feedback; instead of using overall preference, more explicit feedback is provided at the segment level which helps to improve efficacy on long-form question answering, reduce toxicity, and enables LM customization.
8) Hierarchical Vision Transformer - pretrains vision transformers with a visual pretext task (MAE), while removing unnecessary components from a state-of-the-art multi-stage vision transformer; this enables a simple hierarchical vision transformer that’s more accurate and faster at inference and during training.
9) Humor in ChatGPT - explores ChatGPT’s capabilities to grasp and reproduce humor; finds that over 90% of 1008 generated jokes were the same 25 jokes and that ChatGPT is also overfitted to a particular joke structure.
10) Imitating Reasoning Process of Larger LLMs - develops a 13B parameter model that learns to imitate the reasoning process of large foundational models like GPT-4; it leverages large-scale and diverse imitation data and surpasses instruction-tuned models such as Vicuna-13B in zero-shot reasoning.
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍7❤1
Applications of Transformers
New survey paper highlighting major applications of Transformers for deep learning tasks. Includes a comprehensive list of Transformer models.
arxiv.org/abs/2306.07303
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
New survey paper highlighting major applications of Transformers for deep learning tasks. Includes a comprehensive list of Transformer models.
arxiv.org/abs/2306.07303
#مقاله
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
🔥3
Exploring the MIT Mathematics and EECS Curriculum Using LLMs
"GPT-3.5 successfully solves a third of the entire MIT curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate on a test set excluding questions based on images."
arxiv.org/abs/2306.08997
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
"GPT-3.5 successfully solves a third of the entire MIT curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate on a test set excluding questions based on images."
arxiv.org/abs/2306.08997
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍1
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Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
https://ai.facebook.com/blog/voicebox-generative-ai-model-speech/
Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context.
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
https://ai.facebook.com/blog/voicebox-generative-ai-model-speech/
Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context.
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Can LLMs Teach Weaker Agents?
Aligned teachers can intervene w/ free-text explanations using Theory of Mind (ExpUtility+Personalization) to improve students on future unexplained data🙂
Misaligned teachers hurt students😢
arxiv.org/abs/2306.09299
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
Aligned teachers can intervene w/ free-text explanations using Theory of Mind (ExpUtility+Personalization) to improve students on future unexplained data🙂
Misaligned teachers hurt students😢
arxiv.org/abs/2306.09299
#مقاله #ایده_جذاب
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
میخواید اخبار و مقالات و... راجب استارت اپ ها و کمپانیها دریافت کنید اینجا ثبت نام کنید
https://www.joinsuperhuman.ai/subscribe
#خبر
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
https://www.joinsuperhuman.ai/subscribe
#خبر
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
👍1
رایگان امتحان بدید و رایگان آموزش ببینید
https://lightning.ai/pages/ai-education/deep-learning-fundamentals/
#یادگیری_عمیق #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind
https://lightning.ai/pages/ai-education/deep-learning-fundamentals/
#یادگیری_عمیق #منابع #منابع_پیشنهادی
🔸 مطالب بیشتر 👇👇
✅ @AI_DeepMind