• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
• Dataset Condensation with Distribution Matching
• Convolution for Computer Science People
• Score-Based Diffusion Models | Fan Pu Zeng
• NVIDIA LLM Developer AI Day
• PoisonGPT: How to poison LLM supply chainon Hugging Face
• Hamming, "You and Your Research" (June 6, 1995)
• sigstore
• GitHub - andyzoujm/representation-engineering: Representation Engineering: A Top-Down Approach to AI Transparency
• Representation Engineering: A Top-Down Approach to AI Transparency
• GitHub - guardrails-ai/guardrails: Adding guardrails to large language models.
• Guardrails AI | Your Enterprise AI needs Guardrails
• MetNet-3: A state-of-the-art neural weather model available in Google products
• chiphuyen's list / Cool LLM repos
• Idempotent Generative Network
• PromptIDE
• Adversarial Attacks on LLMs
• Evaluation & Hallucination Detection for Abstractive Summaries
• What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone
• Introducing Pika 1.0, An Idea-to-Video Platform
• My North Star for the Future of AI
• Gemini - Google DeepMind
• Consistency Models
• Gemini - Google DeepMind
• The Gemini Lie
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Perspectives on the State and Future of Deep Learning - 2023
• WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 � Hugging Face
• Archives - colah's blog
• Developing Llama 2 | Angela Fan
• Double descent - Wikipedia
• Highly accurate protein structure prediction with AlphaFold - Nature
• Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
• Highly accurate protein structure prediction with AlphaFold - Nature
• Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
• skfolio
• Optimize PyTorch Performance for Speed and Memory Efficiency (2022) | by Jack Chih-Hsu Lin | in Towards Data Science - Freedium
• AlphaGeometry: An Olympiad-level AI system for geometry
• The Faiss library
• Generative Agents: Interactive Simulacra of Human Behavior
• UniVTG: Towards Unified Video-Language Temporal Grounding
• HOW HARD IS TROJAN DETECTION IN DNNS? FOOLING DETECTORS WITH EVASIVE TROJANS
• Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms
• 4 Autonomous AI Agents you need to know
• Image Restoration with Mean-Reverting Stochastic Differential Equations
• CS25 I Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning
• LLM Attacks
• AIM and continuous value data could transform computing
• Adversarial Examples Are Not Bugs, They Are Features
• On Adaptive Attacks to Adversarial Example Defenses
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• Llama2
• Attention is Turing Complete
• The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks
• Perspectives on diffusion
• Deep Dive into Kernel Fusion: Accelerating Inference in Llama V2 - Lefebvre Sarrut's AI blog
• Yam Peleg on Twitter
• Matthias Niessner on Twitter
• Keras: Deep Learning for humans
• Whose responsibility is responsible AI?
• LLM trojan
• Tight Auditing of Differentially Private Machine Learning
• Adversarial training and robustness for multiple perturbations
• No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
• "Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
• Poisoning Web-Scale Training Datasets is Practical
• Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
• Label-Only Membership Inference Attacks
• New ways of breaking app-integrated LLMs
• Drew Linsley on Twitter
• Grammatical Error Correction: Tag, Not Rewrite
• How we built it: Stripe Radar
• Inside GitHub: Working with the LLMs behind GitHub Copilot | The GitHub Blog
• Reconstructing indoor spaces with NeRF
• Xerox scanners/photocopiers randomly alter numbers in scanned documents
• AGI safety difficult
• Nicolas Papernot on Twitter
• Navigating the Challenges of LLMs: Guardrails AI to the Rescue
• Parameter-Free Optimizers for Pytorch
• Sponge Examples: Energy-Latency Attacks on Neural Networks
• Washing The Unwashable : On The (Im)possibility of Fairwashing Detection
• The Security Hole at the Heart of ChatGPT and Bing
• Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
• prompt injection in large language models
• PyPI Repository Under Attack
• Global and surrogate methods, interpretable models
• Local post hoc methods
• Writing Python like it’s Rust
• SAP/ml-model-watermarking
• Where is the Information in a Deep Neural Network?
• Confident Learning: Estimating Uncertainty in Dataset Labels
• Compromised PyTorch Dependency Chain
• Machine Language Modelling from System Loggin
• A tutorial on Differential Evolution with Python
• Faster Deep Learning Training with PyTorch – a 2021 Guide
• Model Calibration
• Investigating the Nature of 3D Generalization in Deep Neural Networks
• Transformers Agents
• ImageBind: One Embedding Space To Bind Them All
• Unlimiformer: Long-Range Transformers with Unlimited Length Input
• Product Launch 2023 Keynote
• HuggingChat
• Meta AI on Semi Supervised Learning
• NeRFs on Google Search
• Interpretability of Transformers with up to two layers of attention
• Using Softmax Linear Units(SoLU) to investigate interpretability of transformers
• Beyond automatic differentiation
• Cultivating Your Research Taste
• Choose Your Weapon: Survival Strategies for Depressed AI Academics
• Approximating Wasserstein distances with PyTorch
• Adversarial Data Augmentation with Chained Transformations (AdvChain)
• ModelDiff: A Framework for Comparing Learning Algorithms
• Stochastic Weight Averaging — a New Way to Get State of the Art Results in Deep Learning
• 30B model now needs only 5.8GB of RAM? How?
• Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Spies, Microsoft, & Enlightenment
• Watermarking for Out-of-distribution Detection
• Continual Few-Shot Learning Using HyperTransformers
• NeurIPS 2022 Workshop MLSW Submissions
• Creating Confidence Intervals for Machine Learning Classifiers
• 26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone
• tinygrad: A simple and powerful neural network framework
• GPT in 60 Lines of NumPy | Jay Mody
• System 2 Is What We Need
• Quick tour - BlindLlama
• Validating LLM Outputs
• The Rise and Potential of Large Language Model Based Agents: A Survey
• Slack
• GitHub - laiyer-ai/llm-guard: The Security Toolkit for LLM Interactions
• Laiyer: Unleash LLM�s potential with confidence
• Introduction to AI Accountability & Transparency Series
• FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
• Visualizing PyTorch memory usage over time
• A tale of two problem solvers (Average cube shadows)
• From Newton�s method to Newton�s fractal (which Newton knew nothing about)
• Full Event | #MicrosoftEvent September 21, 2023
• The Adventure of the Errant Hardware
• Writing Python like it�s Rust
• A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
• GitHub - guardrails-ai/guardrails: Adding guardrails to large language models.
• Guardrails AI | Your Enterprise AI needs Guardrails
• Dataset Condensation with Distribution Matching
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
• AutoPrompt
• Representation Engineering: A Top-Down Approach to AI Transparency
• Work & Projects Summary | CAIS
• Scaling up learning across many different robot types
• Rewind Pendant
• NVIDIA Technical Blog | News and tutorials for developers, data scientists, and IT admins
• Google Colaboratory
• Neel Nanda
• Generating Synthetic Dataset for RAG � Nextra
• The AI research job market shit show (and my experience)
• keerthanapg
• AI's Underbelly: The Zero-Day Goldmineby: Dan McInerney
• huntr - The world�s first bug bounty platform for AI/ML
• GitHub - jxmorris12/vec2text: utilities for decoding deep representations (like sentence embeddings) back to text
• Compiling NumPy code into C++ or CUDA via torch.compile
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
• Dataset Condensation with Distribution Matching
• Convolution for Computer Science People
• Score-Based Diffusion Models | Fan Pu Zeng
• PoisonGPT: How to poison LLM supply chainon Hugging Face
• Hamming, "You and Your Research" (June 6, 1995)
• sigstore
• GitHub - andyzoujm/representation-engineering: Representation Engineering: A Top-Down Approach to AI Transparency
• Representation Engineering: A Top-Down Approach to AI Transparency