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Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers Support this podcast: https://podcasters.spotify.com/pod/s ...
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[QA] Modularity in Transformers: Investigating Neuron Separability & Specialization
6:48
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https://arxiv.org/abs//2408.17324 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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Modularity in Transformers: Investigating Neuron Separability & Specialization
6:42
6:42
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https://arxiv.org/abs//2408.17324 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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[QA] Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
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https://arxiv.org/abs//2408.16737 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
18:07
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https://arxiv.org/abs//2408.16737 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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[QA] Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models
7:55
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Dolphin is an energy-efficient decoder-decoder architecture for processing long contexts in language models, achieving significant improvements in energy efficiency and latency while maintaining response quality. https://arxiv.org/abs//2408.15518 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…
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Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models
16:54
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Dolphin is an energy-efficient decoder-decoder architecture for processing long contexts in language models, achieving significant improvements in energy efficiency and latency while maintaining response quality. https://arxiv.org/abs//2408.15518 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…
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This project proposes three modifications to CycleGAN's pixel-level cycle consistency, improving image quality and reducing artifacts in unpaired image-to-image translation tasks. https://arxiv.org/abs//2408.15374 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us…
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This project proposes three modifications to CycleGAN's pixel-level cycle consistency, improving image quality and reducing artifacts in unpaired image-to-image translation tasks. https://arxiv.org/abs//2408.15374 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us…
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[QA] The Mamba in the Llama: Distilling and Accelerating Hybrid Models
8:15
8:15
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The paper demonstrates distilling large Transformer models into efficient linear RNNs, achieving competitive performance in language tasks while enhancing deployment efficiency and inference speed with limited resources. https://arxiv.org/abs//2408.15237 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Appl…
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The Mamba in the Llama: Distilling and Accelerating Hybrid Models
22:21
22:21
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The paper demonstrates distilling large Transformer models into efficient linear RNNs, achieving competitive performance in language tasks while enhancing deployment efficiency and inference speed with limited resources. https://arxiv.org/abs//2408.15237 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Appl…
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[QA] Generative Verifiers: Reward Modeling as Next-Token Prediction
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https://arxiv.org/abs//2408.15240 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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Generative Verifiers: Reward Modeling as Next-Token Prediction
16:23
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https://arxiv.org/abs//2408.15240 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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[QA] Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
8:25
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This paper explores the correlation between learning rate, batch size, and training tokens, proposing a new Power scheduler that optimizes performance across various model sizes and architectures. https://arxiv.org/abs//2408.13359 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podc…
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Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
13:02
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This paper explores the correlation between learning rate, batch size, and training tokens, proposing a new Power scheduler that optimizes performance across various model sizes and architectures. https://arxiv.org/abs//2408.13359 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podc…
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[QA] A Law of Next-Token Prediction in Large Language Models
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This paper presents a quantitative law governing contextualized token embeddings in LLMs, revealing equal contributions from all layers to prediction accuracy, enhancing understanding and guiding LLM development practices. https://arxiv.org/abs//2408.13442 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Ap…
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A Law of Next-Token Prediction in Large Language Models
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This paper presents a quantitative law governing contextualized token embeddings in LLMs, revealing equal contributions from all layers to prediction accuracy, enhancing understanding and guiding LLM development practices. https://arxiv.org/abs//2408.13442 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Ap…
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[QA] SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
8:29
8:29
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This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection. https://arxiv.org/abs//2408.12748 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
9:51
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This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection. https://arxiv.org/abs//2408.12748 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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[QA] How Diffusion Models Learn to Factorize and Compose
8:14
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This study explores how diffusion models learn compositional representations through controlled experiments, revealing their ability to encode features but limited interpolation over unseen values, enhancing training efficiency. https://arxiv.org/abs//2408.13256 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…
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How Diffusion Models Learn to Factorize and Compose
20:36
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This study explores how diffusion models learn compositional representations through controlled experiments, revealing their ability to encode features but limited interpolation over unseen values, enhancing training efficiency. https://arxiv.org/abs//2408.13256 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…
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[QA] FERRET: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
7:48
7:48
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FERRET enhances adversarial prompt generation for large language models, improving attack success rates and efficiency over RAINBOW TEAMING while ensuring effective prompts across various model sizes. https://arxiv.org/abs//2408.10701 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…
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FERRET: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
17:44
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FERRET enhances adversarial prompt generation for large language models, improving attack success rates and efficiency over RAINBOW TEAMING while ensuring effective prompts across various model sizes. https://arxiv.org/abs//2408.10701 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…
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[QA] Scalable Autoregressive Image Generation with Mamba
7:11
7:11
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AiM is an autoregressive image generative model using Mamba architecture, achieving superior quality and speed in image generation while maintaining efficient long-sequence modeling capabilities. https://arxiv.org/abs//2408.12245 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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Scalable Autoregressive Image Generation with Mamba
17:27
17:27
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AiM is an autoregressive image generative model using Mamba architecture, achieving superior quality and speed in image generation while maintaining efficient long-sequence modeling capabilities. https://arxiv.org/abs//2408.12245 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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[QA] TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
7:55
7:55
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The paper investigates LLMs' challenges with real-world tabular data, proposing the TableBench benchmark and TABLELLM model, highlighting significant gaps between academic performance and industrial application. https://arxiv.org/abs//2408.09174 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…
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TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
21:59
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The paper investigates LLMs' challenges with real-world tabular data, proposing the TableBench benchmark and TABLELLM model, highlighting significant gaps between academic performance and industrial application. https://arxiv.org/abs//2408.09174 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…
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[QA] FocusLLM: Scaling LLM's Context by Parallel Decoding
7:35
7:35
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FocusLLM enhances decoder-only LLMs by efficiently processing long contexts, improving performance on long-context tasks while reducing training costs and maintaining strong language modeling capabilities. https://arxiv.org/abs//2408.11745 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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FocusLLM: Scaling LLM's Context by Parallel Decoding
20:55
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FocusLLM enhances decoder-only LLMs by efficiently processing long contexts, improving performance on long-context tasks while reducing training costs and maintaining strong language modeling capabilities. https://arxiv.org/abs//2408.11745 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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[QA] Sapiens: Foundation for Human Vision Models
7:49
7:49
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Sapiens is a versatile model family for human-centric vision tasks, achieving state-of-the-art performance through self-supervised pretraining and scalable design, excelling in pose estimation, segmentation, depth, and normal prediction. https://arxiv.org/abs//2408.12569 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…
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Sapiens: Foundation for Human Vision Models
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Sapiens is a versatile model family for human-centric vision tasks, achieving state-of-the-art performance through self-supervised pretraining and scalable design, excelling in pose estimation, segmentation, depth, and normal prediction. https://arxiv.org/abs//2408.12569 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…
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[QA] Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
7:25
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Show-o is a unified transformer model that integrates multimodal understanding and generation, outperforming existing models in various vision-language tasks while supporting diverse input-output modalities. https://arxiv.org/abs//2408.12528 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…
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Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
28:14
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Show-o is a unified transformer model that integrates multimodal understanding and generation, outperforming existing models in various vision-language tasks while supporting diverse input-output modalities. https://arxiv.org/abs//2408.12528 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…
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[QA] Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
7:22
7:22
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Jamba-1.5 introduces instruction-tuned large language models with high throughput, low memory usage, and extensive context length, outperforming competitors while being publicly available under an open model license. https://arxiv.org/abs//2408.12570 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…
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Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
16:53
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Jamba-1.5 introduces instruction-tuned large language models with high throughput, low memory usage, and extensive context length, outperforming competitors while being publicly available under an open model license. https://arxiv.org/abs//2408.12570 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…
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Hermes 3 is a neutrally-aligned instruct-tuned model with strong reasoning and creativity, achieving state-of-the-art performance on benchmarks, with weights available on Hugging Face. https://arxiv.org/abs//2408.11857 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…
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Hermes 3 is a neutrally-aligned instruct-tuned model with strong reasoning and creativity, achieving state-of-the-art performance on benchmarks, with weights available on Hugging Face. https://arxiv.org/abs//2408.11857 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…
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[QA] LLM Pruning and Distillation in Practice: The Minitron Approach
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7:23
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https://arxiv.org/abs//2408.11796 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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LLM Pruning and Distillation in Practice: The Minitron Approach
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https://arxiv.org/abs//2408.11796 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…
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[QA] Approaching Deep Learning through the Spectral Dynamics of Weights
7:26
7:26
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This paper explores spectral dynamics of weights in deep learning, revealing optimization biases, enhancing weight decay effects, and distinguishing between memorizing and generalizing networks across various tasks. https://arxiv.org/abs//2408.11804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…
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Approaching Deep Learning through the Spectral Dynamics of Weights
26:45
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This paper explores spectral dynamics of weights in deep learning, revealing optimization biases, enhancing weight decay effects, and distinguishing between memorizing and generalizing networks across various tasks. https://arxiv.org/abs//2408.11804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…
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[QA] Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations
8:04
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The paper challenges the Linear Representation Hypothesis, showing that gated recurrent neural networks encode token sequences using magnitude rather than direction, suggesting broader interpretability in neural network research. https://arxiv.org/abs//2408.10920 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pa…
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Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations
21:03
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The paper challenges the Linear Representation Hypothesis, showing that gated recurrent neural networks encode token sequences using magnitude rather than direction, suggesting broader interpretability in neural network research. https://arxiv.org/abs//2408.10920 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pa…
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[QA] Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
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Transfusion is a multi-modal training method combining language modeling and diffusion, achieving superior performance in generating images and text with models up to 7B parameters. https://arxiv.org/abs//2408.11039 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
24:23
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Transfusion is a multi-modal training method combining language modeling and diffusion, achieving superior performance in generating images and text with models up to 7B parameters. https://arxiv.org/abs//2408.11039 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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[QA] Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
8:37
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The paper presents MOHAWK, a method for distilling Transformers into state space models, achieving strong performance with significantly less training data and computational resources. https://arxiv.org/abs//2408.10189 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…
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Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
31:52
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The paper presents MOHAWK, a method for distilling Transformers into state space models, achieving strong performance with significantly less training data and computational resources. https://arxiv.org/abs//2408.10189 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…
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[QA] JPEG-LM: LLMs as Image Generators with Canonical Codec Representations
7:47
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This paper proposes using canonical codecs for image and video generation in autoregressive models, demonstrating improved efficiency and effectiveness over traditional pixel-based and vector quantization methods. https://arxiv.org/abs//2408.08459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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JPEG-LM: LLMs as Image Generators with Canonical Codec Representations
20:04
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This paper proposes using canonical codecs for image and video generation in autoregressive models, demonstrating improved efficiency and effectiveness over traditional pixel-based and vector quantization methods. https://arxiv.org/abs//2408.08459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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[QA] TextCAVs: Debugging vision models using text
7:19
7:19
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TextCAVs is a novel method for generating concept activation vectors using text descriptions, reducing the need for labeled image data in deep learning model interpretability, particularly in medical applications. https://arxiv.org/abs//2408.08652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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TextCAVs: Debugging vision models using text
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TextCAVs is a novel method for generating concept activation vectors using text descriptions, reducing the need for labeled image data in deep learning model interpretability, particularly in medical applications. https://arxiv.org/abs//2408.08652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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