Zhipu AI has released GLM-4.7-Flash, a 30B-A3B MoE model designed for efficient local coding and agent applications. It offers strong coding and reasoning performance with a 128k token context length and supports English and Chinese.
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes: 3B, 8B, and 14B parameters. For each model size, we release three variants: a pretrained base model for general-purpose use, an instruction finetuned, and a reasoning model for complex problem-solving.
This blog post details how to implement high-performance matrix multiplication using NVIDIA cuTile, focusing on Tile loading, computation, storage, and block-level parallel programming. It also covers best practices for Tile programming and performance optimization strategies.
This article presents a compelling argument that the Manifold-Constrained Hyper-Connections (mHC) method in deep learning isn't just a mathematical trick, but a fundamentally physics-inspired approach rooted in the principle of energy conservation.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
This Python code demonstrates a neural network application on a CircuitPython board, utilizing a camera (OV7670) for image capture, preprocessing, and inference using a digit classifier. It includes image conversion, auto-cropping, and normalization steps.
Train your neural network in TensorFlow or PyTorch, and run it inside CircuitPython using a single line of Python code.
This article details research into finding the optimal architecture for small language models (70M parameters), exploring depth-width tradeoffs, comparing different architectures, and introducing Dhara-70M, a diffusion model offering 3.8x faster throughput with improved factuality.
A deep dive into the process of LLM inference, covering tokenization, transformer architecture, KV caching, and optimization techniques for efficient text generation.
This book provides an introductory, textbook-like treatment of multi-armed bandits. It covers various algorithms and techniques for decision-making under uncertainty, with a focus on theoretical foundations and practical applications.
* **Multi-Armed Bandit Framework:** The document introduces the core concept of multi-armed bandits – a model for decision-making under uncertainty, often used as a simplified starting point for more complex reinforcement learning problems.
* **Applications:** It highlights several applications, including news website optimization, dynamic pricing, and medical trials.
* **Key Concepts:** Defines crucial concepts like arms, rewards, regret, exploration vs. exploitation, and different feedback mechanisms (bandit, full, partial).
* **Algorithms:** Presents and analyzes simple algorithms like Explore-First and Epsilon-Greedy.
* **Regret Bounds:** Focuses heavily on bounding the regret of these algorithms, which measures how much worse the algorithm performs compared to always choosing the best arm.
* **Adaptive Exploration:** Introduces the idea of improving performance through adaptive exploration strategies (adjusting exploration based on observed rewards).
* **Clean Event:** Introduces the concept of the "clean event" to simplify analysis by focusing on high probability events.
* **Table of Contents:** Shows a detailed table of contents, indicating the breadth of topics covered in the full book including Bayesian Bandits, Contextual bandits, Adversarial bandits and connection with economics.
* **Multi-Armed Bandit Framework:** The document introduces the core concept of multi-armed bandits – a model for decision-making under uncertainty, often used as a simplified starting point for more complex reinforcement learning problems.
* **Applications:** It highlights several applications, including news website optimization, dynamic pricing, and medical trials.
* **Key Concepts:** Defines crucial concepts like arms, rewards, regret, exploration vs. exploitation, and different feedback mechanisms (bandit, full, partial).
* **Algorithms:** Presents and analyzes simple algorithms like Explore-First and Epsilon-Greedy.
* **Regret Bounds:** Focuses heavily on bounding the regret of these algorithms, which measures how much worse the algorithm performs compared to always choosing the best arm.
* **Adaptive Exploration:** Introduces the idea of improving performance through adaptive exploration strategies (adjusting exploration based on observed rewards).
* **Clean Event:** Introduces the concept of the "clean event" to simplify analysis by focusing on high probability events.
* **Table of Contents:** Shows a detailed table of contents, indicating the breadth of topics covered in the full book including Bayesian Bandits, Contextual bandits, Adversarial bandits and connection with economics.
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. This book provides a more introductory, textbook-like treatment of the subject, covering IID and adversarial rewards, contextual bandits, and connections to economics.
