deep-learning

This paper proposes the Activity Chain Encoder (ACE), a self-supervised model that learns daily activity patterns from mobile phone location data without labels, and demonstrates its effectiveness in identifying differentiated activity patterns in urban environments like London.

The paper introduces GeoPair, a training-free framework for transformer compression that optimizes cross-layer factorizations while preserving activation geometries, achieving state-of-the-art results across diverse architectures.

Stanford University announced a new deep learning course, CS312, with a unique grading system emphasizing quizzes and practical experiments over assignments.

An interactive tool that visually explains the architecture of Transformer models, using GPT-2 to illustrate key components like embedding, attention mechanisms, and output predictions.

A blog post by Günter Klambauer providing an illustrated overview of recurrence in neural networks, tracing its history from Amari-Hopfield Nets to mentions of GPT-6 Astra and categorizing various architectures based on recurrence patterns.

This paper proposes PainNAS, a block-based Neural Architecture Search method that efficiently reduces computational complexity in Leave-One-Subject-Out evaluation for automated pain assessment systems.

MIST introduces a novel multimodal approach for survival prediction in oncology by using genomic-guided histology attention, showing improved performance across multiple external datasets.

This paper proposes a generative approach using neural temporal point processes for probabilistic forecasting in business process monitoring, demonstrating superior calibration and inference efficiency over existing discriminative models.

The paper proposes risk-aware occupancy as a dense representation for safety in end-to-end autonomous driving, introducing the ROIDrive network and RiskOcc4D-nuScenes dataset, which significantly reduces collision rates.

This paper introduces GVPO++, a novel post-training method for large language models that integrates KL-constrained reward maximization, enhancing training stability and enabling on-policy distillation.

This paper introduces Stiefel-AdamW, a geometry-aware optimizer for linear factorization blocks in deep learning that enhances stability and performance, validated on models like GPT2, ViT, and Mistral 7B.

This paper establishes two limitations of Maximal Coding Rate Reduction (MCR²) for out-of-distribution generalisation, showing that it can fail under distribution shift and that incorporating invariance principles does not eliminate this failure.

A comparative study of temporal deep learning architectures for physiological emotion recognition using multimodal wearable datasets, evaluating LSTM, TCN, and Transformer models under different sensing configurations.

TALON introduces a temporally aware longitudinal framework for radiology report generation that adaptively integrates variable-length patient histories using a dual-channel temporal fusion module to improve accuracy in detecting interval changes and persistent findings.

This article explains why the backpropagation algorithm in neural networks is implemented in a backward pass, clarifying the efficiency advantages over a forward computation based on the chain rule.

The author fine-tuned Qwen3.5 4B using LoRA with public and synthetic data to create a Jev-style model, achieving improved performance and open-sourcing the model and dataset.

The article introduces a visualization tool for the attention mechanism in Transformers, allowing users to customize input matrices and see step-by-step computations of attention scores. It is an initial version with plans for future updates based on user feedback.

The PyTorch Conference North America is announced, featuring sessions on new optimizers like Muon and TorchJD, tools like fastsafetensors and LMCache, with speakers from major tech companies and institutions, happening in San Jose on October 20-21.

An announcement for a talk at PyTorch Conference North America covering practical implementations of Muon, Dion, and Dion3 optimizers in training stacks.

This Twitter thread introduces Hugging Face's Kernels, a tool that allows users to select and replace optimized kernel implementations for supported layers in AI models without rewriting the entire model.