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Revisiting Weight Decay: Beyond Regularization in Modern Deep Learning
Weight decay and ℓ2 regularization are crucial in machine learning, especially in limiting network capacity and reducing irrelevant weight components. These techniques align with Occam’s razor principles and are central to discussions on generalization bounds. However, recent studies have questioned the correlation between norm-based measures and generalization in deep networks. Although weight decay is widely…
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14 Best WIRED Tested and Reviewed Espresso Machines (2024)
Turning your kitchen into a café is a great way to learn (or hone) the art of making the perfect shot.
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Circuit Breakers for AI: Interrupting Harmful Outputs Through Representation Engineering
The adversarial attacks and defenses for LLMs encompass a wide range of techniques and strategies. Manually crafted and automated red teaming methods expose vulnerabilities, while white box access reveals potential for prefilling attacks. Defense approaches include RLHF, DPO, prompt optimization, and adversarial training. Inference-time defenses and representation engineering show promise but face limitations. The control…
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This AI Paper from China Introduces a Reward-Robust Reinforcement Learning from Human Feedback RLHF Framework for Enhancing the Stability and Performance of Large Language Models
Reinforcement Learning from Human Feedback (RLHF) has emerged as a vital technique in aligning large language models (LLMs) with human values and expectations. It plays a critical role in ensuring that AI systems behave in understandable and trustworthy ways. RLHF enhances the capabilities of LLMs by training them based on feedback that allows models to…
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JailbreakBench: An Open Sourced Benchmark for Jailbreaking Large Language Models (LLMs)
Large Language Models (LLMs) are vulnerable to jailbreak attacks, which can generate offensive, immoral, or otherwise improper information. By taking advantage of LLM flaws, these attacks go beyond the safety precautions meant to prevent offensive or hazardous outputs from being generated. Jailbreak attack evaluation is a very difficult procedure, and existing benchmarks and evaluation methods…
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Salesforce AI Introduces SFR-Judge: A Family of Three Judge Models of 8-Billion Parameters 8B, 12B, and 70B Size, Built with Meta Llama 3 and Mistral NeMO
The advancement of large language models (LLMs) in natural language processing has significantly improved various domains. As more complex models are developed, evaluating their outputs accurately becomes essential. Traditionally, human evaluations have been the standard approach for assessing quality, but this process is time consuming and needs to be more scalable for the rapid pace…
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SELMA: A Novel AI Approach to Enhance Text-to-Image Generation Models Using Auto-Generated Data and Skill-Specific Learning Techniques
Text-to-image (T2I) models have seen rapid progress in recent years, allowing the generation of complex images based on natural language inputs. However, even state-of-the-art T2I models need help accurately capture and reflect all the semantics in given prompts, leading to images that may miss crucial details, such as multiple subjects or specific spatial relationships. For…
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Multi-View and Multi-Scale Alignment (MaMA): Advancing Mammography with Contrastive Learning and Visual-Language Pre-training
Multi-View and Multi-Scale Alignment for Mammography Contrastive Learning:Contrastive Language-Image Pre-training (CLIP) has shown potential in medical imaging, but its application to mammography faces challenges due to limited labeled data, high-resolution images, and imbalanced datasets. This study introduces the first full adaptation of CLIP to mammography through a new framework called Multi-view and Multi-scale Alignment (MaMA).…
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Why AI is a know-it-all know nothing
Today’s AI systems can’t earn our trust by sharing the reasoning behind what they say, because there is no such reasoning.Read More
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Practical Lossless Text Compression: FineZip Delivers 54x Speed Boost via Large Language Models
Although the connection between language modeling and data compression has been recognized for some time, current Large Language Models (LLMs) are not typically used for practical text compression due to their lengthy processing times. For example, LLMZip, a recent compression system based on the LLaMA3-8B model, requires 9.5 days to compress just 10 MB of…