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 • Jun 8, 2025 • arxiv.org • Hao Tang, Chengchao Shen
Large multimodal models (LMMs) suffer significant computational challenges due to the high cost of Large Language Models (LLMs) and the quadratic complexity of processing long vision token sequence...
Paper • Sep 22, 2025 • arxiv.org • Chengchao Shen, Hourun Zhu, Gongfan Fang, Jianxin Wang, Xinchao Wang
Transformer models achieve excellent scaling property, where the performance is improved with the increment of model capacity. However, large-scale model parameters lead to an unaffordable cost of ...
Other • Nov 23, 2015 • attendemia.com • Unknown Author
We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectifi...
Paper • Feb 19, 2014 • arxiv.org • Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succ...
Paper • Feb 19, 2015 • arxiv.org • Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, ...
Paper • May 30, 2015 • arxiv.org • Minh-Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT ...
Paper • Dec 11, 2014 • arxiv.org • Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long s...
Paper • Jun 24, 2014 • arxiv.org • Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent n...
Paper • Jul 22, 2015 • arxiv.org • Oriol Vinyals, Quoc Le
Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., boo...
Paper • Nov 17, 2015 • arxiv.org • Andrej Karpathy, Justin Johnson, Li Fei-Fei
Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine lear...
Paper • Jun 22, 2015 • arxiv.org • Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural image...
Paper • May 17, 2015 • arxiv.org • Hyeonwoo Noh, Seunghoon Hong, Bohyung Han
We propose a novel semantic segmentation algorithm by learning a deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The deconvolution netw...
Paper • Dec 1, 2015 • arxiv.org • Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush
We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a hig...
Paper • May 30, 2015 • arxiv.org • Kai Sheng Tai, Richard Socher, Christopher D. Manning
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have...
Paper • Feb 15, 2016 • arxiv.org • Song Han, Huizi Mao, William J. Dally
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduc...
Paper • Mar 5, 2016 • arxiv.org • Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network architecture which p...
Paper • Dec 31, 2015 • arxiv.org • Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progres...
Paper • Jul 28, 2016 • arxiv.org • Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger
Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highl...
Paper • Aug 1, 2015 • arxiv.org • Jan Hosang, Rodrigo Benenson, Piotr Dollár, Bernt Schiele
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and wides...
Paper • Dec 4, 2014 • arxiv.org • Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval. This system is based on a region proposa...
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