Neural Networks

Qualcomm AI Hub provides an end-to-end workflow for deploying machine learning models on Qualcomm devices, as demonstrated in a new tutorial that walks through MobileNet-V2 classification and YOLOv7 object detection. The…

Researchers at CVPR 2026 introduced a training-free single-image diffusion model that generates images matching the patch distribution of a single reference image without neural network training or optimization. The meth…

NVIDIA acquired Kumo AI for over $400 million to integrate graph neural network software into its enterprise AI offerings. The technology targets prediction tasks involving relational data, such as supply chains and frau…

A developer explains that an LLM is fundamentally a function that predicts the next chunk of text, with all intelligent-seeming behavior emerging from repeating this process billions of times. The model tokenizes input i…

A developer has released a compact, hackable implementation of a generative pretrained transformer (GPT) model that operates on byte-level tokens, enabling it to process not only text but also DNA sequences, images, audi…

Large language models (LLMs) frequently exhibit miscalibration, where their reported confidence scores do not match actual accuracy rates, with studies showing mean calibration scores as low as 23.9% in biomedical models…

Developers can optimize spaCy's text processing speed by selectively disabling pipeline components like the dependency parser and lemmatizer when only named entity recognition is needed. The technique reduces processing …

OpenAI's GPT-3.5-Turbo model drops from 90% accuracy to 50% when the correct answer is placed in the middle of a 20,000-token prompt rather than at the start or end, according to research by Liu et al. (2023) published a…

RQM+ and Jordi Labs released Lumo, an in-silico tool that uses machine learning to predict LC-MS and GC-MS response factors for extractables and leachables analysis. The tool routes compounds to chemistry class-specific …

TorchRec's KeyedJaggedTensor, designed to efficiently combine sparse features in recommendation systems without padding, introduces GPU-CPU synchronization that degrades system performance. The data type's output shape c…

Researchers introduced generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The tripl…

A new study shows that discrete Gradient Descent (GD) with a large step size restores symmetry in multi-pathway Deep Linear Networks, counteracting the "winner-takes-all" specialization predicted by Gradient Flow. Resear…

Researchers introduced Branched Neural Rough Differential Equations (B-NRDEs), a Hopf-algebraic framework that extends neural rough differential equations to handle Itô dynamics and manifold-valued data. The method uses …

Researchers have developed ReGeN, a reference-guided generative pipeline that creates synthetic multivariate time series data by decomposing observed sequences into periodic backbones, stochastic residuals, and cross-var…

A study of behavioral foundation models trained on user event sequences found that a small embedder using only 2% of parameters is compute-optimal across all tested budgets, as embedder parameters are more expensive per …

Researchers at arXiv propose a hybrid pre-training objective for language models that combines a Joint Embedding Predictive Architecture (JEPA) latent-space prediction loss with masked language modelling (MLM). Pre-train…

Researchers have developed a data-efficient flood depth prediction method that uses domain-aware coreset selection and tabular foundation models, achieving near-real-time accuracy with only 0.7% of the training data typi…

Researchers introduced trajectory extrapolation error, a measure of how much a language model's hidden states deviate from a linear path during word processing, and found it independently predicts human reading times bey…

Researchers have developed the Multi-Granularity Reasoning Network (MGRN), a new framework for natural language inference that explicitly leverages hierarchical semantic features across multiple levels of language unders…

Researchers have developed a non-autoregressive punctuation restoration method for streaming automatic speech recognition (ASR) systems that uses a weighted lookahead scoring approach with bounded future context. The met…