id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
eadf6c9c394be4bddd136cbdd22aeb4bfa060d637186ae3491a97dc68738b88b | [
"arxiv",
"semantic_scholar"
] | Catastrophic forgetting: still a problem for DNNs | We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning.... | [
"B. Pfülb",
"A. Gepperth",
"S. Abdullah",
"A. Kilian"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2019-05-20T00:00:00 | https://arxiv.org/abs/1905.08077 | https://arxiv.org/pdf/1905.08077v1 | 1905.08077 | 10.1007/978-3-030-01418-6_48 | 25 | 1 | false | null | International Conference on Artificial Neural Networks | 0.3537 |
e8c445b9d0c94a1430d378f7aac1993bc592d155ef21abe15d11589df56794aa | [
"arxiv",
"semantic_scholar"
] | Alpha MAML: Adaptive Model-Agnostic Meta-Learning | Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinf... | [
"Harkirat Singh Behl",
"Atılım Güneş Baydin",
"Philip H. S. Torr"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-05-17T00:00:00 | https://arxiv.org/abs/1905.07435 | https://arxiv.org/pdf/1905.07435v1 | 1905.07435 | null | 72 | 0 | false | null | arXiv.org | 0.4658 |
082cba3250274d6083c0bb86e58265294263f8f0b4c823ff38c1fb10cc27471a | [
"arxiv",
"semantic_scholar"
] | TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning | Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and... | [
"Sung Whan Yoon",
"Jun Seo",
"Jaekyun Moon"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-05-16T00:00:00 | https://arxiv.org/abs/1905.06549 | https://arxiv.org/pdf/1905.06549v2 | 1905.06549 | null | 301 | 18 | false | null | International Conference on Machine Learning | 0.6394 |
5c02d84e68caf25f598335610edc7aa2fcc39b062657f50763cd58e53df026e0 | [
"arxiv",
"semantic_scholar"
] | A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems | This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical component... | [
"David Laredo",
"Zhaoyin Chen",
"Oliver Schütze",
"Jian-Qiao Sun"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics",
"Medicine"
] | 2019-05-15T00:00:00 | https://arxiv.org/abs/1905.05918 | https://arxiv.org/pdf/1905.05918v1 | 1905.05918 | 10.1016/j.neunet.2019.04.016 | 54 | 0 | false | null | Neural Networks | 0.4351 |
14812c009f5d54c4f935f395d9d7ee0328a0b521dc122e25f4616d78aa56e296 | [
"arxiv",
"semantic_scholar"
] | Embeddings and Representation Learning for Structured Data | Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recursive, and convolutional neural networks. Re... | [
"Benjamin Paaßen",
"Claudio Gallicchio",
"Alessio Micheli",
"Alessandro Sperduti"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-05-15T00:00:00 | https://arxiv.org/abs/1905.06147 | https://arxiv.org/pdf/1905.06147v1 | 1905.06147 | null | 8 | 0 | false | null | The European Symposium on Artificial Neural Networks | 0.2386 |
579d2c3beafcec2368f9388660d630eb629837e1ec23db6fc887bb61d6a892ef | [
"arxiv",
"semantic_scholar"
] | Learning Generative Models across Incomparable Spaces | Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension)... | [
"Charlotte Bunne",
"David Alvarez-Melis",
"Andreas Krause",
"Stefanie Jegelka"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-05-14T00:00:00 | https://arxiv.org/abs/1905.05461 | https://arxiv.org/pdf/1905.05461v2 | 1905.05461 | 10.3929/ETHZ-B-000382654 | 119 | 7 | false | null | International Conference on Machine Learning | 0.5198 |
5ff1a020dabc8c4b4303a2940c0804a72ae739dfed1099bd9fde0cef68b75c12 | [
"arxiv",
"semantic_scholar"
] | Fast and Reliable Architecture Selection for Convolutional Neural Networks | The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example. Especially in smaller networks and applications with limited computational resources, optimisation is key. We present a fast and efficient approach for CNN a... | [
"Lukas Hahn",
"Lutz Roese-Koerner",
"Klaus Friedrichs",
"Anton Kummert"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2019-05-06T00:00:00 | https://arxiv.org/abs/1905.01924 | https://arxiv.org/pdf/1905.01924v1 | 1905.01924 | null | 0 | 0 | false | null | The European Symposium on Artificial Neural Networks | 0 |
595392074956175759cd3d497d5f1e86e0c41b4d1c169e9040d8d40b2179ff2d | [
"arxiv",
"semantic_scholar"
] | Unsupervised Representation Learning with Minimax Distance Measures | We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerica... | [
"Morteza Haghir Chehreghani"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-04-27T00:00:00 | https://arxiv.org/abs/1904.13223 | https://arxiv.org/pdf/1904.13223v3 | 1904.13223 | 10.1007/s10994-020-05886-4 | 14 | 1 | false | null | Machine-mediated learning | 0.294 |
3e9bd46f4be18aa89a38f0dd89329b85639be35f39d44d4b8e5f44951a77c110 | [
"arxiv",
"semantic_scholar"
] | Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients | Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting. When using Bayesian models in continu... | [
"Yu Chen",
"Tom Diethe",
"Neil Lawrence"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-04-24T00:00:00 | https://arxiv.org/abs/1904.10644 | https://arxiv.org/pdf/1904.10644v1 | 1904.10644 | null | 15 | 2 | false | null | arXiv.org | 0.301 |
1cb9a9ce979b3482fee857ad5402ced1877e1ce99d5667454038ff3c0e4bbddb | [
"arxiv",
"semantic_scholar"
] | Continual Learning with Self-Organizing Maps | Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-specific stationary environments where they are trained only once on large-scale controlled data repositories. When exposed to non-stationary learning environments, current neural netwo... | [
"Pouya Bashivan",
"Martin Schrimpf",
"Robert Ajemian",
"Irina Rish",
"Matthew Riemer",
"Yuhai Tu"
] | [
"cs.NE"
] | [
"Computer Science"
] | 2019-04-19T00:00:00 | https://arxiv.org/abs/1904.09330 | https://arxiv.org/pdf/1904.09330v1 | 1904.09330 | null | 17 | 0 | false | null | arXiv.org | 0.3138 |
fdc836043ed8ecd0768fd4db515dff29c2f66cf329b020cdefc67e5dafc9cb87 | [
"arxiv",
"semantic_scholar"
] | Three scenarios for continual learning | Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly c... | [
"Gido M. van de Ven",
"Andreas S. Tolias"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-04-15T00:00:00 | https://arxiv.org/abs/1904.07734 | https://arxiv.org/pdf/1904.07734v1 | 1904.07734 | null | 1,057 | 79 | false | null | arXiv.org | 0.9515 |
48a2e72678690b407ffe680449b22b8333b8511c044200551c9614e7e668679d | [
"arxiv",
"semantic_scholar"
] | Transfer Learning with Sparse Associative Memories | In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design Deep Neural Networks which support incremental learning and that can be (partially) trained in real time on embedded devi... | [
"Quentin Jodelet",
"Vincent Gripon",
"Masafumi Hagiwara"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-04-04T00:00:00 | https://arxiv.org/abs/1904.02420 | https://arxiv.org/pdf/1904.02420v3 | 1904.02420 | 10.1007/978-3-030-30487-4_39 | 0 | 0 | false | null | International Conference on Artificial Neural Networks | 0 |
6985f1e254553076b92eebf33f8ad47453098cb8957d14b7c32ca7a22f22311a | [
"arxiv",
"semantic_scholar"
] | Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting | Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting proble... | [
"Xilai Li",
"Yingbo Zhou",
"Tianfu Wu",
"Richard Socher",
"Caiming Xiong"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science",
"Mathematics"
] | 2019-03-31T00:00:00 | https://arxiv.org/abs/1904.00310 | https://arxiv.org/pdf/1904.00310v3 | 1904.00310 | null | 525 | 16 | false | null | International Conference on Machine Learning | 0.6802 |
cc8e1bed4d2dd901fcf87784e8c17dd87adbb0ce2cc1a4c302ba2e74c4085c19 | [
"arxiv",
"semantic_scholar"
] | On-line learning dynamics of ReLU neural networks using statistical physics techniques | We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed from statistical physics. For the first experiments, numerical solutions reveal similar behavior compared to sigmoidal activation researched ... | [
"Michiel Straat",
"Michael Biehl"
] | [
"cs.LG",
"cond-mat.dis-nn",
"stat.ML"
] | [
"Computer Science",
"Physics",
"Mathematics"
] | 2019-03-18T00:00:00 | https://arxiv.org/abs/1903.07378 | https://arxiv.org/pdf/1903.07378v1 | 1903.07378 | null | 11 | 0 | false | null | The European Symposium on Artificial Neural Networks | 0.2698 |
2a28853e157a7139e2a487d0006fd719f3e513b09b2e5d52936e6c4443987e59 | [
"arxiv",
"semantic_scholar"
] | Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation | Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server c... | [
"Yang Chen",
"Xiaoyan Sun",
"Yaochu Jin"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"stat.ML"
] | [
"Computer Science",
"Mathematics",
"Medicine"
] | 2019-03-18T00:00:00 | https://arxiv.org/abs/1903.07424 | https://arxiv.org/pdf/1903.07424v1 | 1903.07424 | 10.1109/TNNLS.2019.2953131 | 527 | 28 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.7312 |
8c42a0cebcdd399fed324369a7fc5d72d7bc69c26140e624c375caff4abca227 | [
"arxiv",
"semantic_scholar"
] | Continual Learning in Practice | This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and ... | [
"Tom Diethe",
"Tom Borchert",
"Eno Thereska",
"Borja Balle",
"Neil Lawrence"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2019-03-12T00:00:00 | https://arxiv.org/abs/1903.05202 | https://arxiv.org/pdf/1903.05202v2 | 1903.05202 | null | 78 | 5 | false | null | Neural Information Processing Systems | 0.4744 |
a9c86f17ad94c38deb380c073f1bb6afc2b7a99ece866a4659cd98b6f23816a4 | [
"arxiv",
"semantic_scholar"
] | Continual Learning via Neural Pruning | We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on neuronal model sparsification. In this method, subsequent tasks are trained using the inactive neurons and filters of the sparsified network and cause zero deterioration to the performanc... | [
"Siavash Golkar",
"Michael Kagan",
"Kyunghyun Cho"
] | [
"cs.LG",
"cs.NE",
"q-bio.NC",
"stat.ML"
] | [
"Computer Science",
"Biology",
"Mathematics"
] | 2019-03-11T00:00:00 | https://arxiv.org/abs/1903.04476 | https://arxiv.org/pdf/1903.04476v1 | 1903.04476 | null | 181 | 10 | false | null | arXiv.org | 0.565 |
1a9a3535c46cebee31e28ad7fd39f9761baa271e8c45975afac5df4d99f68a7c | [
"arxiv",
"semantic_scholar"
] | Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay | Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the... | [
"Mohammad Rostami",
"Soheil Kolouri",
"Praveen K. Pilly"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-03-11T00:00:00 | https://arxiv.org/abs/1903.04566 | https://arxiv.org/pdf/1903.04566v2 | 1903.04566 | 10.24963/ijcai.2019/463 | 82 | 1 | false | null | International Joint Conference on Artificial Intelligence | 0.4798 |
69305eb94930e06b6f7fb0b02c6706b4a82086da4a2ba2061ee43568837d87a2 | [
"arxiv",
"semantic_scholar"
] | Interpolation Consistency Training for Semi-Supervised Learning | We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points... | [
"Vikas Verma",
"Kenji Kawaguchi",
"Alex Lamb",
"Juho Kannala",
"Arno Solin",
"Yoshua Bengio",
"David Lopez-Paz"
] | [
"stat.ML",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Medicine",
"Mathematics"
] | 2019-03-09T00:00:00 | https://arxiv.org/abs/1903.03825 | https://arxiv.org/pdf/1903.03825v5 | 1903.03825 | 10.1016/j.neunet.2021.10.008 | 911 | 105 | false | null | International Joint Conference on Artificial Intelligence | 1 |
5f3b16e3874ed26bd53f48886b51b43c8b56084760f4dd5d057d031496f24a2e | [
"arxiv",
"semantic_scholar"
] | Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening | Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficie... | [
"Arindam Paul",
"Dipendra Jha",
"Reda Al-Bahrani",
"Wei-keng Liao",
"Alok Choudhary",
"Ankit Agrawal"
] | [
"cs.LG",
"physics.chem-ph",
"stat.ML"
] | [
"Computer Science",
"Physics",
"Mathematics"
] | 2019-03-07T00:00:00 | https://arxiv.org/abs/1903.03178 | https://arxiv.org/pdf/1903.03178v4 | 1903.03178 | 10.1109/IJCNN.2019.8852446 | 22 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.3404 |
4764c15a5844e41937eec236a617e3bd4af382fe4860672b36e4c6cb0598e85a | [
"arxiv",
"semantic_scholar"
] | PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers | There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT). Despite the unique representational power of these neural embedding models, it is not clear how the search strategy in th... | [
"Saeed Amizadeh",
"Sergiy Matusevych",
"Markus Weimer"
] | [
"cs.LG",
"cs.LO",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-03-05T00:00:00 | https://arxiv.org/abs/1903.01969 | https://arxiv.org/pdf/1903.01969v1 | 1903.01969 | null | 23 | 3 | false | null | arXiv.org | 0.3451 |
c52d6cc088146c56d993eafc17c4b2d87dfd3123c121493079e8eb99c153a1ac | [
"arxiv",
"semantic_scholar"
] | Scalable and Order-robust Continual Learning with Additive Parameter Decomposition | While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number o... | [
"Jaehong Yoon",
"Saehoon Kim",
"Eunho Yang",
"Sung Ju Hwang"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-02-25T00:00:00 | https://arxiv.org/abs/1902.09432 | https://arxiv.org/pdf/1902.09432v3 | 1902.09432 | null | 209 | 20 | false | null | International Conference on Learning Representations | 0.6611 |
e70ab08b1fa983c335ffd2a37a9f8eb013e74096d323fbcd32088efdd9359ac3 | [
"arxiv",
"semantic_scholar"
] | Deep Bayesian Multi-Target Learning for Recommender Systems | With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this f... | [
"Qi Wang",
"Zhihui Ji",
"Huasheng Liu",
"Binqiang Zhao"
] | [
"cs.LG",
"cs.IR",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-02-25T00:00:00 | https://arxiv.org/abs/1902.09154 | https://arxiv.org/pdf/1902.09154v1 | 1902.09154 | null | 14 | 0 | false | null | arXiv.org | 0.294 |
87e6f5f778c6c9d5b97c59bcd76177dec6f12328e87f630311628dda440fe4f2 | [
"arxiv",
"semantic_scholar"
] | Differentially Private Continual Learning | Catastrophic forgetting can be a significant problem for institutions that must delete historic data for privacy reasons. For example, hospitals might not be able to retain patient data permanently. But neural networks trained on recent data alone will tend to forget lessons learned on old data. We present a differenti... | [
"Sebastian Farquhar",
"Yarin Gal"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2019-02-18T00:00:00 | https://arxiv.org/abs/1902.06497 | https://arxiv.org/pdf/1902.06497v1 | 1902.06497 | null | 12 | 1 | false | null | arXiv.org | 0.2785 |
81fab21e758913394f5a79a92a7435887e4f080ef4663d3b624063d37e254589 | [
"arxiv",
"semantic_scholar"
] | A Unifying Bayesian View of Continual Learning | Some machine learning applications require continual learning - where data comes in a sequence of datasets, each is used for training and then permanently discarded. From a Bayesian perspective, continual learning seems straightforward: Given the model posterior one would simply use this as the prior for the next task.... | [
"Sebastian Farquhar",
"Yarin Gal"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2019-02-18T00:00:00 | https://arxiv.org/abs/1902.06494 | https://arxiv.org/pdf/1902.06494v1 | 1902.06494 | null | 80 | 7 | false | null | arXiv.org | 0.4771 |
d904892f020e2e45c11f27e6cb00fb01337d6a95791c834a4be5a2df37ecf5c6 | [
"arxiv",
"semantic_scholar"
] | Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation | Several recent trends in machine learning theory and practice, from the design of state-of-the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under stochastic gradient descent (SGD), have found it fruitful to study wide random neural networks. Central to these approaches are certain scaling... | [
"Greg Yang"
] | [
"cs.NE",
"cond-mat.dis-nn",
"cs.LG",
"math-ph",
"stat.ML"
] | [
"Computer Science",
"Physics",
"Mathematics"
] | 2019-02-13T00:00:00 | https://arxiv.org/abs/1902.04760 | https://arxiv.org/pdf/1902.04760v3 | 1902.04760 | null | 311 | 36 | false | null | arXiv.org | 0.7841 |
106f6a9ebe3ffdedaa5d5af5565185ad0f20edbda8549eadff0a1cf8d91aa1e8 | [
"arxiv",
"semantic_scholar"
] | Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks | Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails to meet this criterion, making lifelong learning a difficult challeng... | [
"Jason M. Allred",
"Kaushik Roy"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science",
"Medicine"
] | 2019-02-08T00:00:00 | https://arxiv.org/abs/1902.03187 | https://arxiv.org/pdf/1902.03187v2 | 1902.03187 | 10.3389/fnins.2020.00007 | 38 | 4 | false | null | Frontiers in Neuroscience | 0.3978 |
79b5a8b6b873d83ad123b3eca4bf14a5e1d76a1f4543b819c5e863ff8d3cba62 | [
"arxiv",
"semantic_scholar"
] | On ADMM in Deep Learning: Convergence and Saturation-Avoidance | In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADMM pair}), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of ... | [
"Jinshan Zeng",
"Shao-Bo Lin",
"Yuan Yao",
"Ding-Xuan Zhou"
] | [
"cs.LG",
"math.OC",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-02-06T00:00:00 | https://arxiv.org/abs/1902.02060 | https://arxiv.org/pdf/1902.02060v3 | 1902.02060 | null | 38 | 3 | false | null | Journal of machine learning research | 0.3978 |
92023f6ecc2141ec54bb624b3d940a31f0dcb2a83ccb4bcc8aeee8589c247f87 | [
"arxiv",
"semantic_scholar"
] | Generalisation dynamics of online learning in over-parameterised neural networks | Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient de... | [
"Sebastian Goldt",
"Madhu S. Advani",
"Andrew M. Saxe",
"Florent Krzakala",
"Lenka Zdeborová"
] | [
"stat.ML",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.LG"
] | [
"Computer Science",
"Mathematics",
"Physics"
] | 2019-01-25T00:00:00 | https://arxiv.org/abs/1901.09085 | https://arxiv.org/pdf/1901.09085v1 | 1901.09085 | null | 15 | 0 | false | null | arXiv.org | 0.301 |
1ad24db00ba0caf229ef7fd0954100ba486e66f7bc4fbea36e105d579cadd032 | [
"arxiv",
"semantic_scholar"
] | Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract) | This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses interpretable visual concepts to explain features in middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the explaine... | [
"Quanshi Zhang",
"Yu Yang",
"Ying Nian Wu"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2019-01-21T00:00:00 | https://arxiv.org/abs/1901.07538 | https://arxiv.org/pdf/1901.07538v1 | 1901.07538 | null | 1 | 0 | false | null | arXiv.org | 0.0753 |
8c815760bbedba307ef530e4b189758ccd7995c56ecf61034fe7200add06d0f7 | [
"arxiv",
"semantic_scholar"
] | Hierarchical Attentional Hybrid Neural Networks for Document Classification | Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not take into account the c... | [
"Jader Abreu",
"Luis Fred",
"David Macêdo",
"Cleber Zanchettin"
] | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2019-01-20T00:00:00 | https://arxiv.org/abs/1901.06610 | https://arxiv.org/pdf/1901.06610v2 | 1901.06610 | 10.1007/978-3-030-30493-5_39 | 38 | 5 | false | null | International Conference on Artificial Neural Networks | 0.3978 |
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