deep-learning

The article discusses the increasing capabilities and improvements in AI video models, highlighting their growing effectiveness in generating or processing video content.

At TechCrunch Disrupt 2026, Ricursive Intelligence's co-founders Anna Goldie and Azalia Mirhoseini will discuss how AI can automate and accelerate chip design, potentially reducing the design cycle from years to weeks.

A tweet sharing a GitHub repository that compiles explanations for key machine learning papers, such as Transformer, BERT, and GPT, for educational purposes.

The article explains the core mechanism of how neural networks learn, detailing forward propagation and backward propagation using the chain rule to compute gradients and adjust parameters.

The paper proposes XACT, a framework for learning sparse attribution masks over time-frequency transforms to explain time-series classifiers, demonstrating improved precision and interpretability over baselines.

The paper introduces the Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model that enhances accuracy and robustness in computational fluid dynamics simulations through innovations like Residual Wavelet Mamba and physics-informed loss.

This study explores the integration of distributed acoustic sensing and deep learning for monitoring urban traffic dynamics with high spatiotemporal resolution. A deep learning framework is developed to analyze DAS data for vehicle detection and traffic state inference.

This paper proposes a leakage-safe machine learning protocol using region-held-out evaluation to detect hydrogen embrittlement in 316L stainless steel from SEM micrographs, finding that LBP+SVM outperforms deep learning methods.

SpaFactor is a lightweight framework that predicts spatial transcriptomics from histology images by integrating spatial context and gene programs, demonstrating improved accuracy and biological fidelity across multiple cohorts.

This paper presents a validation protocol for knowledge tracing models, evaluating predictive performance and the stability and faithfulness of explanations using XGBoost and deep learning baselines on ASSISTments datasets.

The PyTorch Foundation is hosting an Introduction Track and PyTorch Associate Training at PyTorch Conference North America 2026 to help developers, researchers, and engineers enhance their technical skills in deep learning and AI.

ROOSTER is a shared module that learns alignment between condition and target sequences for time-series forecasting and PPG-to-vital-sign reconstruction, achieving superior performance across multiple benchmarks.

The paper presents Neurogenesis Network (NGN), a differentiable parameterization for learning the optimal size of neural networks during training, applicable to various architectures like MLPs, CNNs, and Transformers.

This paper conducts a scaling study for fMRI foundation models, revealing that performance depends on the combination of pretraining data size, model size, and training duration, not just compute.

This paper introduces a dual-model masking metric to benchmark ten explainable methods for temporal attribution in sequential recommendation systems, finding that gradient-based methods like GradientSHAP and Integrated Gradients yield the most faithful and robust attributions.

The paper proposes DissipNet, a deep discrete-time dissipative recurrent neural network that explicitly enforces dissipativity through structural constraints to ensure stable modeling of dissipative systems, outperforming traditional RNNs and Physics-Informed Neural Networks.

CoRe-Stack+ is a meta-learning pipeline that improves deep stacking generalization by filtering redundancies and enhancing calibration, achieving better accuracy and efficiency on vision benchmarks.

Quartet introduces a graph transformer architecture with quad-branch cross-attention and a causal random walk sampler to enhance performance on relational graph tasks, outperforming state-of-the-art baselines like RelGT and HGT.

The paper introduces HARN, a hierarchical associative resonance network for event-driven multi-timeframe forecasting in financial time series, showing competitive results against baselines through evaluations on multiple assets.

The paper proposes ADNet, an adaptive decomposition network for multi-step traffic forecasting that learns to disentangle heterogeneous traffic dynamics into dominant and residual components via spectral decomposition, achieving superior performance on the TraffiDent dataset.