This page shows the most relevant public items for Machine Learning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Aug 23, 2016 • arxiv.org • Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve...
Paper • Jul 12, 2012 • arxiv.org • Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng
We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answe...
Paper • Dec 10, 2022 • arxiv.org • Diederik P Kingma, Max Welling
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We...
Paper • May 20, 2015 • arxiv.org • Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveatio...
Paper • Jan 7, 2016 • arxiv.org • Alec Radford, Luke Metz, Soumith Chintala
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less atte...
Paper • Feb 29, 2016 • arxiv.org • Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep n...
Paper • Jul 3, 2012 • arxiv.org • Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting ha...
Paper • Jan 30, 2017 • arxiv.org • Diederik P. Kingma, Jimmy Ba
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to i...
Paper • Mar 2, 2015 • arxiv.org • Sergey Ioffe, Christian Szegedy
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the t...
Paper • Oct 6, 2013 • arxiv.org • Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed...
Paper • Nov 28, 2013 • arxiv.org • Matthew D Zeiler, Rob Fergus
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, o...
Paper • Apr 2, 2015 • arxiv.org • Anh Nguyen, Jason Yosinski, Jeff Clune
Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now...
Paper • Mar 9, 2015 • arxiv.org • Geoffrey Hinton, Oriol Vinyals, Jeff Dean
A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making...
Paper • Feb 13, 2026 • arXiv • Miaosen Zhang, Xu Yang, Qi Dai, Chong Luo
Supervised fine-tuning (SFT) is efficient but often yields inferior generalization compared to RL, a gap driven by RL's use of on-policy data. We propose a framework to bridge this chasm by enablin...
Paper • Feb 8, 2026 • arXiv • Fan Yang, Rui Meng, Trudi Di Qi, Yuxin Wen
We argue that while replacing data with model parameters characterizes the present of Federated Learning (FL), replacing parameters with preferences represents a more scalable and privacy-preservin...
Paper • Feb 12, 2026 • arXiv • Xiaoxiao Wang, Chunxiao Li, Junying Wang, Zicheng Zhang
The paper introduces STAR (STatistical and Agentic Reasoning), a novel framework designed to predict the performance of large models across diverse benchmarks from limited observations. Existing st...
Paper • Feb 12, 2026 • arXiv • Yurong Chen, Yu He, Michael I. Jordan, Fan Yao
Modern language agents must operate over long-horizon, multi-turn interactions, but most rely on full-context prompting which leads to unbounded memory growth. We introduce MEM1, an end-to-end rein...
Paper • Feb 13, 2026 • arXiv • Zhiwei Liu, Yuan He, Hao Peng, Zhiqiang Chen
Recently, diffusion LLMs have emerged as a promising alternative to autoregressive models. Diffusion LLMs address traditional limitations with two advances, including multi-token prediction (i.e., ...
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