This page shows the most relevant public items for cs.LG, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jan 23, 2020 • arXiv • Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, ...
Paper • Jul 20, 2017 • arXiv • John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a 'surrogate' objective...
Paper • Jun 13, 2016 • arXiv • Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
Deep learning algorithms typically require vast amounts of data to achieve high performance, contrasting sharply with human ability to learn new concepts from a single example. We introduce Matchin...
Paper • Jun 9, 2015 • arXiv • Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such proble...
Paper • May 31, 2019 • Science • Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel
Multiplayer video games represent a significant frontier for AI research, requiring real-time, high-dimensional sensory processing, spatial navigation, and team-based coordination. We report an AI ...
Paper • Jan 10, 2023 • arXiv • Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap
General intelligence requires solving tasks across diverse domains without human intervention. We present DreamerV3, a general and scalable reinforcement learning algorithm that masters a wide rang...
Paper • Mar 31, 2020 • arXiv • Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell
Atari 2600 games have been a long-standing benchmark in the reinforcement learning community. While previous algorithms have achieved superhuman performance on average, they consistently fail on a ...
Paper • Dec 8, 2021 • arXiv • Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre
We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 trillion token database, our R...
Paper • Sep 28, 2018 • arXiv • Andrew Brock, Jeff Donahue, Karen Simonyan
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train ...
Paper • Oct 5, 2022 • Nature • Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Pushmeet Kohli
Matrix multiplication is a fundamental computational task, heavily utilized in neural networks, scientific computing, and graphics. Despite its ubiquity, finding optimal algorithms for matrix multi...
Paper • Nov 14, 2023 • Science • Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Alet, Suman Ravuri, Timo Ewalds, Zachary Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, Peter Battaglia
Global medium-range weather forecasting has long been dominated by massive, compute-intensive numerical weather prediction (NWP) models governed by atmospheric physics equations. We present GraphCa...
Paper • Mar 4, 2021 • arXiv • Andrew Jaegle, Felix Gimeno, Andrew Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira
Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, and touch. We introduce the Perceiver, an architecture tha...
Paper • Feb 9, 2018 • arXiv • Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
Scaling reinforcement learning algorithms to utilize thousands of machines efficiently is crucial for tackling complex, visually rich environments. We introduce IMPALA (Importance Weighted Actor-Le...
Paper • Nov 2, 2017 • arXiv • Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu
Learning useful representations without supervision remains a key challenge in machine learning. We propose the Vector Quantised-Variational AutoEncoder (VQ-VAE), a simple yet powerful generative m...
Paper • Feb 26, 2015 • Nature • Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Demis Hassabis
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural networ...
Paper • Feb 20, 2026 • arXiv • J. L. Martinez, Sarah Chen, Arjun Nair
To counter the rise of Agent Hijacking, we propose a framework for 'Verifiable Intent.' This protocol cryptographically anchors the human user's original objective to every sub-task generated by a ...
Paper • Feb 12, 2026 • arXiv • Dulhan Jayalath, Oiwi Parker Jones
Decoding natural language from non-invasive brain recordings like Magnetoencephalography (MEG) remains a significant challenge due to the low signal-to-noise ratio and the scarcity of paired brain-...
Paper • Feb 1, 2026 • arXiv • Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
We present algorithms for diffusion model sampling which obtain δ-error in polylog(1/δ) steps, given access to eO(δ)-accurate score estimates in L2. This is an exponential improvement over all prev...
Paper • Jun 7, 2023 • Nature • Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelpi, Marco Selvi, Alhussein Fawzi
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day. As demand for computation grows, it has become critical for these algorithms to be as performant as p...
Paper • Feb 12, 2026 • arXiv • Ankita Paul, Wenyi Wang
Glioblastoma (GBM) is characterized by high intra-tumoral heterogeneity, which poses a significant challenge for diagnosis and treatment planning. We propose a brain-inspired Topological Neural Net...
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