deep-learning

PyTorch announced a live Q&A webinar for the PyTorch 2.15 release, where Meta engineers will walk through new capabilities including the rewritten NCCL backend, NIXL one-sided transports, finer FSDP2/pipelining memory control, and faster NVFP4/FP8 decoding on NVIDIA Blackwell and Apple Silicon.

MIT researchers Regina Barzilay and Jim Collins won the 2026 Pritzker Prize for AI in Science Research Excellence for their pioneering work using AI to discover antibiotics like halicin and abaucin against drug-resistant pathogens.

The paper introduces CUTTIE, a hierarchical testing method that clusters a trained network's units into a tree and tests them top-down with conditional independence tests to localize predictive concepts (e.g., regulatory DNA motifs) while controlling the family-wise error rate, demonstrating it on a model predicting chromatin accessibility from sequence.

TensorSSM proposes an Implicit Neural Representation for functional tensor decomposition that memorizes spatial positions in a dynamic State Space Model, encoding coarse-to-fine spatial tokens and cross-mode dependencies to improve reconstruction accuracy and storage efficiency across diverse tensor datasets.

The paper explores using large language models (Claude Opus 5) to automatically construct simple baselines for 36 deep learning tasks from Nature/Science/Cell-family studies, finding these baselines recover a median 95.5% of the performance gap and match or exceed the published deep learning model in 12 studies, suggesting automated baseline checking as a scalable safeguard for scientific peer review.

DeepLearning.AI 宣布 AI Dev NYC 大会（11月30日至12月1日，切尔西码头），由 Yann LeCun、Andrew Ng 以及来自 OpenAI、BlackRock、Hugging Face、Neo4j 的工程师讲授评估驱动开发、智能体系统构建与生产环境规模化运维等主题。

Part 6 of an open-source Inference Engineering repo is released, covering KV cache memory allocation, growth, release, and capacity-aware scheduling using real model inference and DynamicCache.

Recommend 10 AI and machine learning courses freely available to the public through Stanford University in 2026 (such as CS221 Artificial Intelligence, CS229 Machine Learning, CS230 Deep Learning, CS234 Reinforcement Learning, CS236 Deep Generative Models, and CS336 Building Large Language Models from Scratch) for self-improvement.

该论文指出权重归一化（WN）训练的收益主要源于其诱导出的有效学习率（ELR）轨迹，而非归一化本身，并据此提出 AIR（仿射平方根倒数衰减）调度策略。在 128B token 的 MoE 预训练实验中，AIR 在训练损失、下游性能和超参数鲁棒性上均优于 cosine 等调度方案。

A Stanford CS 312 "Deep Learning Alchemy" course shifts the burden of theory construction onto students (and their Claude assistants), having them empirically probe transformer components via ablations on a 614M-token depth-8 transformer — prompting reflection that maybe memorized ML knowledge was never that critical.

PyTorch 2.15 发布，包含 3,117 次提交，重写的 NCCL 后端成为默认、新增 NIXL 单向传输、FSDP2 与流水线并行的更细粒度控制，并在 NVIDIA Blackwell（NVFP4）和 Apple Silicon（FP8）上带来更快的 LLM 解码，同时扩展了 AMD、Intel、ROCm 等平台支持。

DeepLearning.AI 新推出课程《Loop Engineering with LangChain Deep Agents》，由 LangChain 的 Sydney Runkle 与 Esmaeil Gargari 授课，讲解围绕 AI 代理构建 agent、验证、事件驱动和 hill-climbing 四种循环的工程方法。

A promotional post for MLCheatSheets, a free open-source collection of 500+ machine learning and deep learning cheatsheets in Russian, including theory, formulas, code examples, a learning roadmap, and exercises.

ReliMorph 提出一个可靠性引导的对称框架，将局部对应评估与空间专家路由及残差速度门控耦合，用于 3D 可变形医学图像配准，并在单模态和多模态任务上超越现有方法。

The paper proposes a comprehensive data generation strategy combining traditional augmentation, CutMix, MixUp, and DCGAN-generated images to address data insufficiency and class imbalance for olive tree disease classification, evaluated using EfficientNetV2B0 and showing notable accuracy improvements.

A multi-site benchmark of empirical, machine-learning (LightGBM) and deep-learning (U-Net) models for satellite-derived bathymetry in the Arabian Gulf, validated leakage-free against 285,000 multibeam soundings, showing that validation design matters more than model choice, with the simple Stumpf ratio outperforming complex models in transfer settings.

The paper introduces H-Glow, a hazard-guided framework that accelerates discrete flow matching (DFM) for graph generation by decomposing Continuous-Time Markov chain generators into hazards and conditional jump distributions, enabling high-quality graph generation with substantially fewer sampling steps on molecular and synthetic benchmarks.

提出了 Augmentation-Guided Adversarial Training (AGAT) 框架，利用基于增强的局部稳定性 (ALS) 得分选择不稳定邻域的样本进行对抗训练，在仅对 20–30% 样本施加对抗监督的情况下达到相当或更高的鲁棒准确率，同时降低训练成本。

该论文定义了一种与 Fisher 信息矩阵对齐的参数分布，使得在保证低损失的同时获得与 Fisher 信息矩阵零特征值数量成正比的参数对数体积，从而得到一个随 Fisher 信息矩阵期望秩缩放的高概率 PAC 泛化误差界。

该论文在Avazu数据集上系统评估了CTR模型的后校准（Platt scaling与isotonic regression）在不同时间切片长度下的效果，发现随机划分严重低估校准误差，且使用约12小时的较宽时间窗拟合校准通常优于最新几小时数据，并建议离线评估应采用多时间原点、多种子的时间化校准评估协议。