# Deep Learning Published articles for Deep Learning. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Decoding cosmic signals with deep learning and Keras DevFeed: [Decoding cosmic signals with deep learning and Keras]() Original publisher: [Read original article]() Author: Yufeng Guo; Jonas Glombitza, PhD Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Deep learning](), [Keras](), [data]() Tags: [analysis](), [blog-post](), [data](), [deep-learning](), [keras](), [particle-physics](), [physics](), [research]() ### AI overview The article explains how deep learning and Keras can help analyze the enormous, complex datasets produced by astroparticle-physics observatories. These methods may improve instrument sensitivity, reveal hidden patterns, and identify anomalies in signals from cosmic messengers such as photons, neutrinos, and cosmic rays. ### Source excerpt Astroparticle physics sits at the exciting intersection of astrophysics and particle physics and stu... ## How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules DevFeed: [How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules]() Original publisher: [Read original article]() Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deep neural networks](), [datasets](), [Code]() Tags: [ai](), [ai-models](), [antibiotics](), [applied-ai](), [chatgpt](), [code](), [codex](), [datasets](), [deep-learning](), [search]() ### AI overview César de la Fuente's lab uses deep-learning models, ChatGPT, and Codex to search genome and protein datasets for antimicrobial candidates that could help fight drug-resistant infections. ### Source excerpt César de la Fuente's lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections. ## Research acceleration: The view inside OpenAI DevFeed: [Research acceleration: The view inside OpenAI]() Original publisher: [Read original article]() Published: 2026-09-06T08:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Research](), [AI-assisted coding](), [coding]() Tags: [agents](), [ai-research](), [coding](), [deep-learning](), [experiments](), [frontier-ai](), [openai](), [research]() ### AI overview OpenAI describes how coding agents are being used throughout its AI research workflow, with reported increases in code contribution, experiment execution, task complexity, and success rates. It frames this progress as a step toward supervised automated AI research while emphasizing human control over research priorities and deployment decisions. ### Source excerpt Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration. ## Mapping global methane emissions from space with deep learning DevFeed: [Mapping global methane emissions from space with deep learning]() Original publisher: [Read original article]() Published: 2026-09-01T18:40:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Google]() Tags: [climate-sustainability](), [deep-learning](), [earth-ai](), [framework](), [global](), [google](), [machine-intelligence](), [mapping](), [nasa](), [research](), [space]() ### AI overview The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements. ### Source excerpt Climate & Sustainability ## D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores DevFeed: [D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-08-31T08:06:13Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Jetson](), [Robotics](), [Embedded Systems](), [Microcontroller](), [Jetson Orin](), [SOC](), [cpu](), [Arm](), [real-time](), [GPU](), [Linux](), [Nvidia]() Tags: [arm](), [artificial-intelligence-ai](), [camera](), [computer-vision](), [cortex-a78](), [cpu](), [deep-learning](), [development-kit](), [dfrobot](), [edge-ai](), [embedded](), [embedded-systems](), [gpu](), [hardware](), [jetson](), [jetson-orin](), [linux](), [mcu](), [networking](), [nvidia](), [os](), [processors](), [real-time](), [robotics](), [ros](), [safety](), [sensor](), [sensors](), [single-board-computer](), [soc](), [software](), [som](), [specifications](), [systems](), [ubuntu]() ### AI overview The D-Robotics RDK S100P is a robotics single-board computer featuring a 6-core Cortex-A78AE CPU, 4-core Cortex-R52+ MCU domain, 128 TOPS INT8 Nash BPU, Mali-G78AE GPU, and 24 GB of LPDDR5. Its MCU supports real-time motor and sensor control, potentially reducing the need for a separate real-time controller depending on the robot's requirements. ### Source excerpt D-Robotics RDK S100P development kit is a robotics SBC built around an S100P-based module providing an alternative to NVIDIA Jetson Orin NX 16GB with a 6-core Cortex-A78AE application cluster, a 4-core Cortex-R52+ MCU domain, a Nash BPU rated at 128 TOPS INT8, a Mali-G78AE GPU, and 24 GB of LPDDR5. The four R52+ cores can be configured for reliable real-time control: two cores run in lockstep for safety, and the other two support split-lock operation. In practice, the A78AE CPU and BPU can handle Linux and vision processing, while the MCU handles time-critical tasks such as motor and sensor I/O. This can reduce the need for a separate real-time controller, although whether it eliminates one depends on the robot and its requirements. RDK S100P specifications: SoC - D-Robotics S100P CPU - 6x Arm Cortex-A78AE @ 2.0 GHz (safety-capable AE cores) MCU - 4x Arm Cortex-R52+ @ 1.2 GHz (1x DCLS [...] The post D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores appeared first on CNX Software - Embedded Systems News. ## PyTorch vs. TensorFlow: Differences, Performance, and How to Choose DevFeed: [PyTorch vs. TensorFlow: Differences, Performance, and How to Choose]() Original publisher: [Read original article]() Author: NICOLAS PIRO, DATA SCIENTIST AND AI DEVELOPER @ TOPTAL Published: 2026-08-27T04:00:00Z Content type: comparison Language: en Sources: [Toptal Blog]() Topics: [Training AI Models]() Tags: [deep-learning](), [deployment](), [experimentation](), [infrastructure](), [performance](), [pytorch](), [tensorflow](), [tooling]() ### AI overview A comparison of PyTorch and TensorFlow for deep-learning experimentation, model design, production workflows, tooling, and infrastructure considerations. ### Source excerpt This comprehensive guide explores how PyTorch and TensorFlow shape deep-learning work in 2026, from experimentation and model design to production workflows, ecosystem tooling, and infrastructure considerations. ## Broadening access to Skala creates a faster path to predictive DFT DevFeed: [Broadening access to Skala creates a faster path to predictive DFT]() Original publisher: [Read original article]() Author: Sebastian Ehlert, Stefano Battaglia, Thijs Vogels, Jan Hermann, Jens Wehner, Giulia Luise, Klaas Giesbertz, Chin-Wei Huang, Aaron Kaplan, Kate Milton, Stephanie Marisa Lanius, Derk Kooi, P. Bernát Sza Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Deep neural networks](), [Benchmark](), [Microsoft](), [data](), [Software]() Tags: [benchmark](), [chemistry](), [data](), [deep-learning](), [integration](), [microsoft](), [performance](), [research](), [research-blog](), [software](), [workflows]() ### AI overview Microsoft Research presents Skala 1.1, a deep-learning exchange-correlation functional that improves accuracy for molecular simulations while expanding access through integrations with major electronic-structure software. A living benchmark will track the computational performance of future releases. ### Source excerpt Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft Research. ## Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery DevFeed: [Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery]() Original publisher: [Read original article]() Published: 2026-08-17T10:34:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Deep learning](), [Machine learning](), [Google]() Tags: [deep-learning](), [general-science](), [google](), [health](), [health-bioscience](), [machine-intelligence](), [machine-learning](), [monitoring](), [multimodal](), [science]() ### AI overview Google Research presents PhotoScan, a deep learning approach that estimates body composition from smartphone photos and predicts insulin resistance with accuracy comparable to DXA scans in a clinical research setting. The article explains how body-composition measures such as fat distribution and visceral fat may complement wearable data for earlier cardiometabolic risk assessment. ### Source excerpt General Science ## 🗓 This Week In AI Research (25-31 July 26) DevFeed: [🗓 This Week In AI Research (25-31 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-07T01:00:43Z Content type: article Language: en Sources: [Into AI]() Topics: [releases](), [Machine learning](), [deepseek](), [foundation-models](), [Mixture of Experts (MoE)](), [lora](), [codex]() Tags: [ai](), [deep-learning](), [deepseek](), [lora](), [ml](), [moe](), [performance](), [releases](), [research]() ### AI overview A weekly roundup of AI research and releases covering DeepSeek-V4-Flash-0731, the Pangram 4 AI-text classification model, the OpenMLE system and its Frontis-MA1-35B agent, and the Metis memory foundation model. ### Source excerpt The top 10 AI research papers and releases this week. ## 34 Amazon Research Awards Build on Trainium recipients announced DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced]() Original publisher: [Read original article]() Author: Amazon Research Awards team Published: 2026-08-05T15:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [Artificial Intelligence](), [responsible-ai](), [AWS AI chips](), [AI Research](), [Optimization](), [Inference](), [moe](), [Large Language Model](), [distributed-systems](), [synthetic-data]() Tags: [academic-ai-funding](), [ai](), [ai-research](), [ai-research-grants](), [ai-safety-and-alignment](), [amazon-research-awards](), [ara](), [aws-ai-chips](), [aws-trainium](), [build-on-trainium](), [data](), [deep-learning](), [distributed-systems](), [inference](), [internal-ara-program-updates](), [llm](), [machine-learning-research](), [mixture-of-experts](), [optimization](), [research](), [responsible-ai](), [synthetic](), [synthetic-data-generation]() ### AI overview Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium. ### Source excerpt Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. ## Adding new quick commands to a smart speaker without degrading existing commands DevFeed: [Adding new quick commands to a smart speaker without degrading existing commands]() Original publisher: [Read original article]() Author: khaymon (Яндекс) Published: 2026-07-23T09:00:04Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [яндекс](), [asr](), [cpu]() Tags: [asr](), [continual-learning](), [cpu](), [deep-learning](), [keyword-spotting](), [machine-learning](), [speech-processing](), [tag-355bb785df82](), [tag-4004cf5948d3](), [tag-8483d32db3b1](), [tag-967d8467ce56](), [tag-9c1e53a23032](), [tag-a1312fd2c7ff](), [tag-d14eb265d33e](), [tag-d346fb5ae499]() ### AI overview The article discusses Yandex's compact on-device neural model for recognizing Alice quick commands. It covers adding track-rating and Bluetooth commands while aiming to preserve performance on existing commands and limit device resource use. ### Source excerpt Чтобы дать команду умной колонке, не обязательно говорить активационное слово "Алиса": есть быстрые команды -- короткие фразы, с помощью которых можно управлять музыкой, громкостью или умным домом. Например, чтобы переключить трек, достаточно просто сказать "дальше", а чтобы убавить звук -- "тише". Весь список команд можно посмотреть в настройках вашего аккаунта в приложении "Дом с Алисой". Быстрые команды удобнее не только пользователям, но и системе: запросы через слово "Алиса" требуют обращения к модели распознавания речи ASR, которой из-за её размеров необходимы серверные вычислительные ресурсы, а модель быстрых команд устроена гораздо компактнее. Она работает прямо на устройстве, а значит, ограничена вычислительными ресурсами самой колонки -- её CPU и оперативной памятью. Из-за этого модель нельзя сильно увеличить: ей приходится оставаться компактной, зато запрос обрабатывается быстрее. За распознавание быстрых команд отвечает нейросеть. Её архитектура почти полностью совпадает с решением для наушников Яндекс Дропс, которое подробно описал в своей статье Григорий Афанасенко. Разница в основном в масштабе: наша модель весит всего от 0,5 до 1,5 МБ в зависимости от железа конкретного устройства. Со временем перед нами встала задача добавить к базовым командам "лайк" и "дизлайк" для управления треками, а также команды "включи блютус" и "выключи блютус". Особенно это актуально для Станции Стрит, которую часто берут с собой на природу, где нет интернета. Но главным было гарантировать абсолютное отсутствие ухудшения на уже запущенных командах и не слишком сильно увеличивать потребление ресурсов на устройстве. Читать далее ## Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning DevFeed: [Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-14T18:20:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Benchmark](), [Chain-of-thought](), [datasets](), [Cloud](), [Google](), [NCCL]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-inference](), [benchmark](), [blackwell](), [chain-of-thought](), [cost](), [deep-learning](), [engineering](), [evaluation](), [featured](), [google](), [kaggle](), [lora](), [nemotron](), [nvidia](), [performance](), [pre-trained-foundation-models](), [synthetic](), [techniques]() ### AI overview The article distills lessons from NVIDIA's Nemotron Model Reasoning Challenge, where more than 5,000 Kaggle participants tested ways to improve AI reasoning under shared model, infrastructure, and evaluation constraints. It highlights synthetic chain-of-thought data, trace quality, targeted solvers, validation beyond public leaderboards, and careful training and context-budget management. ### Source excerpt The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore a focused question: What techniques can improve reasoning accuracy when... ## From Campus to Community Part Two: The Researcher Exodus DevFeed: [From Campus to Community Part Two: The Researcher Exodus]() Original publisher: [Read original article]() Author: Nithya Ruff Published: 2026-07-13T14:07:27Z Content type: opinion Language: en Sources: [Linux Foundation - Blog]() Topics: [Artificial Intelligence](), [linux foundation](), [Open Source](), [Deep learning](), [Computer science](), [Hardware](), [Robotics]() Tags: [ai](), [computer-science](), [deep-learning](), [hardware](), [linux-foundation](), [open-source](), [robotics]() ### AI overview The second part of a Linux Foundation series argues that AI researchers leaving universities for industry jobs, combined with limited academic access to computing resources, is weakening open-source development and academic research. It cites faculty departure data and examples from Carnegie Mellon and other research communities. ### Source excerpt This is a series from Linux Foundation Board Chair, Nithya Ruff. Part One can be found here. ## From pixels to planning: Earth AI for nature restoration DevFeed: [From pixels to planning: Earth AI for nature restoration]() Original publisher: [Read original article]() Published: 2026-06-16T17:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [dataset](), [Deep learning](), [Google](), [data]() Tags: [ai](), [climate-sustainability](), [data](), [deep-learning](), [earth-ai](), [google](), [machine-intelligence](), [open-source-models-datasets](), [research](), [resource](), [sustainability](), [uk](), [vector]() ### AI overview Google Research describes a high-resolution deep learning approach that converts pixel-based maps of fine-scale ecological features into a vectorized dataset. The resource is intended to support nature restoration, carbon accounting, and biodiversity efforts across working landscapes in the UK while considering food security. ### Source excerpt Climate & Sustainability ## Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP DevFeed: [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Rémi Ouazan Reboul; Sergio Paniego; Pedro Cuenca; Sayak Paul Published: 2026-06-11T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [cpu](), [jobs]() Tags: [cpu](), [cuda](), [deep-learning](), [gpu](), [jobs](), [kernels](), [liger](), [mlp](), [open-source](), [profile](), [pytorch](), [torch](), [traces]() ### AI overview A PyTorch profiling tutorial that moves from nn.Linear to a fused multilayer perceptron, explaining GPU kernels, CPU launch overhead, and tensor-transpose behavior in profiler traces. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Towards passive heart health monitoring via smartphone camera DevFeed: [Towards passive heart health monitoring via smartphone camera]() Original publisher: [Read original article]() Published: 2026-06-04T19:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Monitoring](), [webcam](), [dataset](), [Deep learning](), [Google](), [Ground truth / benchmark quality]() Tags: [deep-learning](), [devices](), [google](), [health](), [health-bioscience](), [heart-rate-monitoring](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [model](), [monitoring](), [publication](), [research](), [resource](), [smartphones]() ### AI overview Google Research presents PHRM, a research system that passively estimates heart rate and resting heart rate from facial video captured by a smartphone's front-facing camera during everyday use. The system applies deep learning to video recorded after face unlock events and reports accuracy comparable to electrocardiogram-derived ground truth and wearable trackers. The publication also releases a large, diverse smartphone-video dataset and the pre-trained PHRM-mini model for qualified researchers. ### Source excerpt Health & Bioscience ## Distilling Long-Tail User Behavior into Scalable Embeddings for Job Search DevFeed: [Distilling Long-Tail User Behavior into Scalable Embeddings for Job Search]() Original publisher: [Read original article]() Author: Marsan Ma Published: 2026-06-03T23:28:10Z Content type: article Language: en Sources: [Indeed]() Topics: [recommendation systems](), [Embeddings](), [Deep learning](), [Latency](), [Job]() Tags: [big-data](), [data-science](), [deep-learning](), [embeddings](), [engineering](), [feature-store](), [job-search](), [latency](), [machine-learning](), [performance](), [ranking]() ### AI overview Indeed describes a user behavior modeling system for job search that learns from long-term user histories offline, distills them into fixed-length embeddings, and serves them through a feature store for use by online ranking and recommendation models. The approach is designed to preserve rich behavioral signals while meeting latency and cost constraints. ### Source excerpt Authors : Marsan Ma, Nikhil Lopes, Raj Amrit, Hong Lu, Dipankar Biswas, Trent KyonoLeadership: Iris Wang, Madhu Kurup Recommendation and ranking systems power many of the most important experiences on large internet platforms. Yet the models that run in production are rarely the largest models we can train. They are usually compact, latency-sensitive supervised models [...] ## Physics AI research that's shaping the industry. DevFeed: [Physics AI research that's shaping the industry.]() Original publisher: [Read original article]() Published: 2026-05-27T12:00:05Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [AI Research](), [Physics-guided deep learning](), [Artificial Intelligence](), [data](), [datasets](), [AI Foundation Models](), [GPU](), [Machine learning](), [Transformer]() Tags: [ai](), [ai-research](), [data](), [datasets](), [deep-learning](), [design](), [energy](), [engineering](), [gpu](), [industry](), [machine-learning](), [models](), [performance](), [physics](), [real-time](), [research]() ### AI overview Mistral describes its acquisition of Emmi AI and its focus on Physics AI for industrial engineering. The article surveys published work on CFD, neural surrogates, foundation models, datasets, plasma turbulence, and real-time industrial simulation across aerospace, automotive, semiconductors, and energy. ### Source excerpt Published breakthroughs pushing the state of the art. ## Diverse reasoning traces teach LLMs to make better decisions DevFeed: [Diverse reasoning traces teach LLMs to make better decisions]() Original publisher: [Read original article]() Author: Sheng Jia; Xiao Wang; Shiva Kasiviswanathan Published: 2026-05-26T15:17:06Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Language models]() Tags: [benchmarks](), [chain-of-thought](), [code-generation](), [conversational-ai](), [deep-learning](), [evaluation](), [fine-tuning](), [iclr](), [iclr-2026](), [large-language-models-llms](), [llms](), [math-reasoning](), [parallel-reasoning](), [post-training](), [post-training-optimization](), [reasoning](), [reinforcement-learning](), [traces]() ### AI overview The article presents set-supervised fine tuning and global forking policy optimization to train LLMs on multiple distinct reasoning paths. It reports 5% to 7% single-shot accuracy gains on standard benchmarks. ### Source excerpt How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies. ## Gesture Recognition Based on TFLite DevFeed: [Gesture Recognition Based on TFLite]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [TensorFlow Lite](), [Espressif](), [model-deployment](), [Tensorflow](), [Keras](), [On-device AI]() Tags: [ai](), [blog](), [deep-learning](), [deployment](), [development-board](), [edge-ai](), [edge-computing](), [esp-idf](), [espressif](), [how-to](), [inference](), [keras](), [model-deployment](), [model-training](), [tensorflow-lite]() ### AI overview This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference. ### Source excerpt This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI. ## Where wild things roam: Identifying wildlife with SpeciesNet DevFeed: [Where wild things roam: Identifying wildlife with SpeciesNet]() Original publisher: [Read original article]() Published: 2026-03-06T17:59:38Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [Google](), [Open Source](), [Deep learning](), [Data analysis](), [data](), [migration]() Tags: [ai](), [ai-models](), [analysis](), [climate-sustainability](), [data](), [datasets](), [deep-learning](), [earth-ai](), [generative-ai](), [google](), [migration](), [open-source](), [open-source-models-datasets](), [sustainability]() ### AI overview Google Research describes SpeciesNet, an open-source AI model that identifies nearly 2,500 animal categories in camera-trap images. Trained on 65 million labelled images, it is being used by research groups worldwide to support wildlife monitoring, conservation, and analysis of animal populations and patterns. ### Source excerpt Climate & Sustainability ## Sequential Attention: Making AI models leaner and faster without sacrificing accuracy DevFeed: [Sequential Attention: Making AI models leaner and faster without sacrificing accuracy]() Original publisher: [Read original article]() Published: 2026-02-04T15:14:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Algorithms & Theory](), [Optimization](), [Programming](), [Machine learning](), [Deep learning](), [Google]() Tags: [ai](), [algorithms](), [algorithms-theory](), [deep-learning](), [features](), [google](), [machine-learning](), [optimization](), [research](), [scalability](), [training]() ### AI overview Google Research presents Sequential Attention, a greedy and adaptive subset-selection method for making large-scale machine-learning and deep-learning models more efficient. The approach selects useful components such as features, layers, blocks, embedding chunks, or weight entries during a single training run, reducing redundancy while preserving accuracy and limiting additional training cost. ### Source excerpt Algorithms & Theory ## Unlocking health insights: Estimating advanced walking metrics with smartwatches DevFeed: [Unlocking health insights: Estimating advanced walking metrics with smartwatches]() Original publisher: [Read original article]() Published: 2026-01-15T22:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep learning](), [model architecture](), [data](), [Google]() Tags: [architecture](), [data](), [deep-learning](), [evaluation](), [google](), [health](), [health-bioscience](), [human-computer-interaction-and-visualization](), [model](), [performance](), [portable](), [smartphones](), [validation]() ### AI overview Google researchers report a large-scale validation study showing that consumer smartwatches can accurately estimate comprehensive spatio-temporal gait metrics, including walking speed, step length, and double support time. They describe a multi-output deep learning model using a temporal convolutional network and smartwatch inertial sensor data, with performance comparable to smartphone-based methods. ### Source excerpt Health & Bioscience ## Ten years DevFeed: [Ten years]() Original publisher: [Read original article]() Published: 2025-12-11T00:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [systems](), [Reinforcement learning](), [Language models]() Tags: [ai](), [company](), [deep-learning](), [language](), [openai](), [reinforcement-learning](), [research]() ### AI overview OpenAI reflects on its first ten years, describing its early research culture, breakthroughs in deep learning and reinforcement learning, and work toward building AI systems that benefit humanity. The article also discusses lessons from the organization's development and its continued optimism about its mission. ### Source excerpt OpenAI reflects on ten years of progress, from early research breakthroughs to widely used AI systems that reshaped what's possible. We share lessons from the past decade and why we remain optimistic about building AGI that benefits all of humanity. [Next page]()