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arxiv:1706.02677 | paper | corpus_metadata | null | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel wor... | https://arxiv.org/abs/1706.02677 | 2017-06-08T16:51:53Z | 1706.02677 | 10.48550/arxiv.1706.02677 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1706.02677 | dbebcae61afdf3187cf58190ca43126f49fdfc816b3da835b96bf4551db6503d | 0.764172 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.04552 | paper | corpus_metadata | null | FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis | Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, due to their unique properties but also the health risk they can impose. Unfortunately, image-based analysis of fibers still involves manual annotation, which is a time-consuming and costly process. We therefore propose the use of region-based conv... | https://arxiv.org/abs/2006.04552 | 2020-06-08T13:03:09Z | 2006.04552 | 10.1016/j.powtec.2020.08.034 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.powtec.2020.08.034 | 9a5aa66f7d6e2a008f62d8eb995751a7ce4f3ffe36b359502db9673ce73bb28f | 0.557614 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.11600 | paper | corpus_metadata | null | Knee Point Identification Based on Trade-Off Utility | Knee points, characterised as their smallest trade-off loss at all objectives, are attractive to decision makers in multi-criterion decision-making. In contrast, other Pareto-optimal solutions are less attractive since a small improvement on one objective can lead to a significant degradation on at least one of the oth... | https://arxiv.org/abs/2005.11600 | 2020-05-23T21:05:00Z | 2005.11600 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2005.11600 | 718ffec5e1961d6de6863911f79618ac33f374b4d119281c0b852029c4d17eda | 0.53316 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1606.04080 | paper | corpus_metadata | null | Matching Networks for One Shot Learning | Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from... | https://arxiv.org/abs/1606.04080 | 2016-06-13T19:34:22Z | 1606.04080 | 10.48550/arxiv.1606.04080 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1606.04080 | a40adbf12d3f55d8f358574e478b6f560aab41806fe587f01e2363adc11d3b3d | 0.846538 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.02901 | paper | corpus_metadata | null | Probabilistic FastText for Multi-Sense Word Embeddings | We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation al... | https://arxiv.org/abs/1806.02901 | 2018-06-07T20:57:22Z | 1806.02901 | 10.48550/arxiv.1806.02901 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1806.02901 | 520c3b69f630e8c1af6053c4eb848494ef2cb0f267b38e41d9cda26dd84e4cac | 0.767949 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.05268 | paper | corpus_metadata | null | Self-Supervised Visual Planning with Temporal Skip Connections | In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction: if a robot can learn to predict the future, it can use this p... | https://arxiv.org/abs/1710.05268 | 2017-10-15T02:58:20Z | 1710.05268 | 10.48550/arxiv.1710.05268 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1710.05268 | 569c026681457b901fec336e07451e68dd434a1f44416d91dd8db1adaccb6b9c | 0.728503 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.11164 | paper | corpus_metadata | ShuffleNet v2 | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design | Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct... | https://arxiv.org/abs/1807.11164 | 2018-07-30T04:18:25Z | 1807.11164 | 10.48550/arxiv.1807.11164 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.11164 | 6c4a4c7e9b055d1c8e9f50c10c71fa9141351dd498c4f52481742e7583633a7a | 0.662815 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:2204.13346 | paper | corpus_metadata | null | UniTE: Unified Translation Evaluation | Translation quality evaluation plays a crucial role in machine translation. According to the input format, it is mainly separated into three tasks, i.e., reference-only, source-only and source-reference-combined. Recent methods, despite their promising results, are specifically designed and optimized on one of them. Th... | https://arxiv.org/abs/2204.13346 | 2022-04-28T08:35:26Z | 2204.13346 | 10.18653/v1/2022.acl-long.558 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.18653/v1/2022.acl-long.558 | 88ec7e7a267fe4e1f1b4f5e52aefd52e7441bfde1388847878cbc4b1665f8715 | 0.84182 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.09481 | paper | corpus_metadata | null | Efficient nonparametric statistical inference on population feature importance using Shapley values | The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which measurements to collect in subsequent experiments. Valid statistical inference on this importance is a key component in understanding the popula... | https://arxiv.org/abs/2006.09481 | 2020-06-16T19:47:11Z | 2006.09481 | 10.48550/arxiv.2006.09481 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.09481 | 9f051e862e49d020addc9de6d9fc546fe2c3f20f08fca6488e8bfee84ca08092 | 0.566264 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.02244 | paper | corpus_metadata | null | Searching for MobileNetV3 | We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently ... | https://arxiv.org/abs/1905.02244 | 2019-05-06T19:38:31Z | 1905.02244 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1905.02244 | c7b824eb2e2fbdc8509bd06a5ebffb63b7345e1672d82c9f8a5aab7dece2e5d7 | 0.718333 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.04026 | paper | corpus_metadata | null | Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics | Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of ... | https://arxiv.org/abs/2004.04026 | 2020-04-08T14:50:13Z | 2004.04026 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | arxiv:2004.04026 | a75a326461a36c9c478417ab2b2e85aa2b1d2b86d217714a5f88a0490fa54afe | 0.526637 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1510.02437 | paper | corpus_metadata | null | Distilling Model Knowledge | Top-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate into larger systems. Ideally, we would like to replace such cumbersome models with simpler models that perform equally well... | https://arxiv.org/abs/1510.02437 | 2015-10-08T18:40:50Z | 1510.02437 | 10.48550/arxiv.1510.02437 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1510.02437 | 0584adbaf015cba0332016255fa85de2dae7cdbc7a706f0404db18e4a9c99c4e | 0.773823 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.03762 | paper | corpus_metadata | null | Attention Is All You Need | The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on at... | https://arxiv.org/abs/1706.03762 | 2017-06-12T17:57:34Z | 1706.03762 | 10.65215/2q58a426 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.65215/2q58a426 | b59de13b88b5ffc6071b192a81d384ab15f91d6ef46083f2b0507246974a1301 | 0.839975 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.11164 | paper | corpus_metadata | null | On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs | We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations a... | https://arxiv.org/abs/2101.11164 | 2021-01-27T02:06:09Z | 2101.11164 | 10.1007/978-981-16-2765-1_17 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-981-16-2765-1_17 | 1240dcf07d8ea8f76010ef2b478ae3c697fe8654db3fdb1d707bb37f0588ffb7 | 0.656058 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.02142 | paper | corpus_metadata | null | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning | Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computa... | https://arxiv.org/abs/1506.02142 | 2015-06-06T12:30:43Z | 1506.02142 | 10.48550/arxiv.1506.02142 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1506.02142 | ed8639c935091a5502d7f5c0feb2ec7cf1f2c5eaa654fc086051113567e20f3e | 0.75149 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.09064 | paper | corpus_metadata | null | GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language | Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this ... | https://arxiv.org/abs/1812.09064 | 2018-12-21T11:45:16Z | 1812.09064 | 10.48550/arxiv.1812.09064 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1812.09064 | e34146e365dd147e87c2159dfa0d6e3c1e9e3de0fad7961ef44e0c01a90240f7 | 0.56439 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.11536 | paper | corpus_metadata | null | Tractable higher-order under-approximating AE extensions for non-linear systems | We consider the problem of under and over-approximating the image of general vector-valued functions over bounded sets, and apply the proposed solution to the estimation of reachable sets of uncertain non-linear discrete-time dynamical systems. Such a combination of under and over-approximations is very valuable for th... | https://arxiv.org/abs/2101.11536 | 2021-01-27T16:45:26Z | 2101.11536 | 10.1016/j.ifacol.2021.08.504 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1016/j.ifacol.2021.08.504 | 817039eff1092b7c379fbad45f2ce121611d88e6e442743e4f1b2e2ddfdaf28a | 0.576654 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.11327 | paper | corpus_metadata | null | Lightweight Probabilistic Deep Networks | Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in... | https://arxiv.org/abs/1805.11327 | 2018-05-29T09:40:52Z | 1805.11327 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1805.11327 | 13565fa40ac44e266da7afa55893e346de6cd989e2a3f6d902284af5c185daf0 | 0.645254 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2001.10954 | paper | corpus_metadata | null | Reducing complexity and unidentifiability when modelling human atrial cells | Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is cri... | https://arxiv.org/abs/2001.10954 | 2020-01-29T16:57:07Z | 2001.10954 | 10.1098/rsta.2019.0339 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1098/rsta.2019.0339 | 591a2bfe941fbdd06cd9dcc67853e9aef350fd219023b019eb9262b7b83bf419 | 0.539313 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.03400 | paper | corpus_metadata | null | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on... | https://arxiv.org/abs/1703.03400 | 2017-03-09T18:58:03Z | 1703.03400 | 10.48550/arxiv.1703.03400 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.03400 | 37b461b2a49b96c485a4c0fde13c07d41b3a99a9977cfd0af52a83ef36d7b9a2 | 0.891086 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1606.00709 | paper | corpus_metadata | null | f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization | Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but ... | https://arxiv.org/abs/1606.00709 | 2016-06-02T14:53:33Z | 1606.00709 | 10.48550/arxiv.1606.00709 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1606.00709 | 8abe0f8a3a893f1fcc50135ff5364a247a791cf55648af9e2c0b1408513fe2b5 | 0.711104 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:cs/0205028 | paper | corpus_metadata | null | NLTK: The Natural Language Toolkit | NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing componen... | https://arxiv.org/abs/cs/0205028 | 2002-05-17T12:51:00Z | cs/0205028 | 10.48550/arxiv.cs/0205028 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | null | doi:10.48550/arxiv.cs/0205028 | aeede32d788eb141d81e246787d0b2c93689bc67c09fba27f4454b8aeadc1977 | 0.567027 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1712.03541 | paper | corpus_metadata | null | An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification | Convolutional neural networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of hidden layers consisting of neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping be... | https://arxiv.org/abs/1712.03541 | 2017-12-10T14:50:28Z | 1712.03541 | 10.48550/arxiv.1712.03541 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.1712.03541 | 1cfa1ec0c5156164ad4ebdf2a89295be8d1038947ee9edca5558562b689ce339 | 0.676307 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1702.08835 | paper | corpus_metadata | null | Deep Forest | Current deep learning models are mostly build upon neural networks, i.e., multiple layers of parameterized differentiable nonlinear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules. We conjecture that the mystery behind... | https://arxiv.org/abs/1702.08835 | 2017-02-28T16:10:31Z | 1702.08835 | 10.48550/arxiv.1702.08835 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1702.08835 | d95f0ca53b99a6dd0682cfc1a89a748fc03ac8cd6d747a10d249e27cefc34201 | 0.760831 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.03082 | paper | corpus_metadata | null | A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data | Gated Recurrent Unit (GRU) is a recently-developed variation of the long short-term memory (LSTM) unit, both of which are types of recurrent neural network (RNN). Through empirical evidence, both models have been proven to be effective in a wide variety of machine learning tasks such as natural language processing (Wen... | https://arxiv.org/abs/1709.03082 | 2017-09-10T10:43:09Z | 1709.03082 | 10.1145/3195106.3195117 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.1145/3195106.3195117 | 0ab2d10856522d309d5c3e24b60bbfa15d775bbf26098f3b4ed080f246c7e667 | 0.691364 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2306.12063 | paper | corpus_metadata | null | High Throughput Open-Source Implementation of Wi-Fi 6 and WiMAX LDPC Encoder and Decoder | This paper describes the design and C99 implementation of a free and open-source Low-Density Parity-Check (LDPC) codes encoder and decoder focused primarily on the Quasi-Cyclic LDPC (QCLDPC) codes utilized in the IEEE 802.11ax-2021 (Wi-Fi 6) and IEEE 802.16-2017 (WiMAX) standards. The encoder is designed in two variant... | https://arxiv.org/abs/2306.12063 | 2023-06-21T07:17:50Z | 2306.12063 | 10.48550/arxiv.2306.12063 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2306.12063 | a2a1837b93c60d95affd5ad72f62cbc4891ab6adde58f38bafb22467715404bc | 0.527182 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1604.00788 | paper | corpus_metadata | null | Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models | Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consu... | https://arxiv.org/abs/1604.00788 | 2016-04-04T09:30:54Z | 1604.00788 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1604.00788 | a838ed13921a204e583899f46b7f93ecaf62c286f6b882bd4926fd7fa0e3d8c5 | 0.72332 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.12675 | paper | corpus_metadata | null | Continuous Dropout | Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation pattern... | https://arxiv.org/abs/1911.12675 | 2019-11-28T12:37:48Z | 1911.12675 | 10.48550/arxiv.1911.12675 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.12675 | 7fbec7d292890494be792994f8504eb38376d74b83bbcb9fc8521f920ea67647 | 0.640834 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1907.10110 | paper | corpus_metadata | null | FALCON: An accurate real-time monitor for client-based mobile network data analytics | Network data analysis is the fundamental basis for the development of methods to increase service quality in mobile networks. This requires accurate data of the current load in the network. The control channel analysis is a way to monitor the resource allocations and the throughput of all active subscribers in a public... | https://arxiv.org/abs/1907.10110 | 2019-07-23T19:42:16Z | 1907.10110 | 10.1109/globecom38437.2019.9014096 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/globecom38437.2019.9014096 | ab583225e3b5060077941e0909ecd3eb90ff662d1c2b1926e020ca390f31d911 | 0.511589 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.07394 | paper | corpus_metadata | null | Wavelet Convolutional Neural Networks for Texture Classification | Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding ... | https://arxiv.org/abs/1707.07394 | 2017-07-24T03:59:04Z | 1707.07394 | 10.48550/arxiv.1707.07394 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1707.07394 | 234fbbb637fac7688c36ad9e88854c8e676ba151a0767fda29380519f06290fb | 0.73705 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.03654 | paper | corpus_metadata | null | Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos | Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the underlying geometric rules within stereo videos. Specifically, given two consecutive stereo image pairs from a video, we first estimate dept... | https://arxiv.org/abs/1810.03654 | 2018-10-08T18:49:46Z | 1810.03654 | 10.48550/arxiv.1810.03654 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1810.03654 | 43e26fa3a5c00d3228f6ed98d82e5d743228b4a16338e45c9e9c7d147cf40b9a | 0.717008 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.05544 | paper | corpus_metadata | null | Rgtsvm: Support Vector Machines on a GPU in R | Rgtsvm provides a fast and flexible support vector machine (SVM) implementation for the R language. The distinguishing feature of Rgtsvm is that support vector classification and support vector regression tasks are implemented on a graphical processing unit (GPU), allowing the libraries to scale to millions of examples... | https://arxiv.org/abs/1706.05544 | 2017-06-17T14:22:46Z | 1706.05544 | 10.48550/arxiv.1706.05544 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1706.05544 | e9c6703d061fc5876eec08eb6a0e4b867e72c832e59529070bfe514f5350a81b | 0.562305 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2209.12675 | paper | corpus_metadata | null | Assessing the Role of Datasets in the Generalization of Motion Deblurring Methods to Real Images | Successfully training end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images. Obtaining such datasets remains a challenging task. In this paper, we first review the limitations of existing ... | https://arxiv.org/abs/2209.12675 | 2022-09-26T13:20:35Z | 2209.12675 | 10.48550/arxiv.2209.12675 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2209.12675 | cbfad93539f745c60bc1a39a567056eef6faf0d56c053845b808c2789b848e8a | 0.807002 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.04099 | paper | corpus_metadata | null | Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods | Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a post-processing step by fitting a 3D layout to the layout elements. Unt... | https://arxiv.org/abs/1910.04099 | 2019-10-09T16:22:04Z | 1910.04099 | 10.48550/arxiv.1910.04099 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1910.04099 | 3c6091a0900cc9f7e1ed4347e2b730f5ef9ef7730587010690d9470619bc4d5e | 0.66324 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.03299 | paper | corpus_metadata | null | ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection | This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anoma... | https://arxiv.org/abs/1908.03299 | 2019-08-09T03:52:08Z | 1908.03299 | 10.48550/arxiv.1908.03299 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.03299 | ec87ef93f5a81edb7405ff545d885a41cb125086d79efde81bb098e781a2ff37 | 0.639094 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.03894 | paper | corpus_metadata | null | GNNExplainer: Generating Explanations for Graph Neural Networks | Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models, and explainin... | https://arxiv.org/abs/1903.03894 | 2019-03-10T00:56:26Z | 1903.03894 | 10.48550/arxiv.1903.03894 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1903.03894 | 4e5a3b2bc45e2bb62ba7ecf1ce9cd84ba892b58375cff4d5afd662cdbc689579 | 0.754732 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.12514 | paper | corpus_metadata | null | Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic | In this paper, we describe our team's effort on the semantic text question similarity task of NSURL 2019. Our top performing system utilizes several innovative data augmentation techniques to enlarge the training data. Then, it takes ELMo pre-trained contextual embeddings of the data and feeds them into an ON-LSTM netw... | https://arxiv.org/abs/1912.12514 | 2019-12-28T20:11:33Z | 1912.12514 | 10.48550/arxiv.1912.12514 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1912.12514 | d662f53920b7cffe8cac307e8b763ac3df25c8bafb303133815dc33db1b4ca09 | 0.637496 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.10566 | paper | corpus_metadata | null | Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations | We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of ... | https://arxiv.org/abs/1711.10566 | 2017-11-28T21:29:35Z | 1711.10566 | 10.48550/arxiv.1711.10566 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.10566 | bc1783fdc8c1f13736cbc92bb7950eac3ec4813d708c84c0e15d6d55504870c9 | 0.649584 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.03180 | paper | corpus_metadata | null | Adversarial Robustness Guarantees for Gaussian Processes | Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are not only accurate, but also robust to perturbations. In this paper we present a framework to analyse adversarial robustness of GPs, defined as... | https://arxiv.org/abs/2104.03180 | 2021-04-07T15:14:56Z | 2104.03180 | 10.48550/arxiv.2104.03180 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2104.03180 | 6743c1a89540c2f6db32278d25b58f8a6d6a15b887a73a246678d20a20b88c3b | 0.630615 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1701.04099 | paper | corpus_metadata | null | Field-aware Factorization Machines in a Real-world Online Advertising System | Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this meth... | https://arxiv.org/abs/1701.04099 | 2017-01-15T19:13:22Z | 1701.04099 | 10.48550/arxiv.1701.04099 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1701.04099 | 2b08659e541f7cc8def3bda30ba7d68fe8268b5c16f201d3c5833e26dd165727 | 0.637071 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.11807 | paper | corpus_metadata | null | GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning | We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. ... | https://arxiv.org/abs/2002.11807 | 2020-02-26T21:51:35Z | 2002.11807 | 10.1109/lra.2020.2994035 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/lra.2020.2994035 | b8f7ae097677897ef742d0b6d976a99370ce08066503698810122add46c72f0d | 0.595397 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1409.0473 | paper | corpus_metadata | Neural Machine Translation by Jointly Learning to Align and Translate | Neural Machine Translation by Jointly Learning to Align and Translate | Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural ma... | https://arxiv.org/abs/1409.0473 | 2014-09-01T16:33:02Z | 1409.0473 | 10.48550/arxiv.1409.0473 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1409.0473 | c1741b294e5eb9ee2ad24969a60010e43555d4c250fcb4421fb056529a49bde5 | 0.810695 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models",
"iclr-test-of-time"
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"epoch-all-ai-models:row:3291",
"iclr-test-of-time:award:2025:4"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.09362 | paper | corpus_metadata | null | Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors | Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not... | https://arxiv.org/abs/1811.09362 | 2018-11-23T05:13:38Z | 1811.09362 | 10.48550/arxiv.1811.09362 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.09362 | 85e4813a614fc7f630007b8f04f382d3c5f6ddf9d4a9d39c4d32d360f1564795 | 0.658817 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1603.07285 | paper | corpus_metadata | null | A guide to convolution arithmetic for deep learning | We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers... | https://arxiv.org/abs/1603.07285 | 2016-03-23T17:52:21Z | 1603.07285 | 10.48550/arxiv.1603.07285 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1603.07285 | 119b8cc939eec84e1ff4e9329436600fcffbe19f8925e19324be96b59a642b76 | 0.542568 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.09268 | paper | corpus_metadata | null | MS MARCO: A Human Generated MAchine Reading COmprehension Dataset | We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841... | https://arxiv.org/abs/1611.09268 | 2016-11-28T18:14:11Z | 1611.09268 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1611.09268 | 0b28dcb06eccdfd5130517472e5096ca6c39bc5027f2afa0923c8c01251c25b4 | 0.738073 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.08879 | paper | corpus_metadata | null | Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems | Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with ... | https://arxiv.org/abs/1812.08879 | 2018-12-20T22:53:33Z | 1812.08879 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1812.08879 | 111a5430d04dcd6adfff815d3e3555359322d935da8424094bc2542376b96a0e | 0.754337 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1808.04355 | paper | corpus_metadata | null | Large-Scale Study of Curiosity-Driven Learning | Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrins... | https://arxiv.org/abs/1808.04355 | 2018-08-13T17:58:01Z | 1808.04355 | 10.48550/arxiv.1808.04355 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1808.04355 | 3deded1e72edee5938add67a418d227b1f68e19459accc250b69a72422cea596 | 0.711073 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1405.0312 | paper | corpus_metadata | null | Microsoft COCO: Common Objects in Context | We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. ... | https://arxiv.org/abs/1405.0312 | 2014-05-01T21:43:32Z | 1405.0312 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1405.0312 | 15a16cc4dac76a663efb04031088f1abb8c85ce9ab60efb8c368bbf2ba23febc | 0.633483 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.13277 | paper | corpus_metadata | null | Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks | We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. Each molecule is represented as a complete graph in which each atom is a vertex and all vertex pairs are connected by an edge weighted by the Euclidean distance between atom pairs. We ingest a... | https://arxiv.org/abs/2106.13277 | 2021-06-24T19:01:05Z | 2106.13277 | 10.48550/arxiv.2106.13277 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2106.13277 | 5b47323f2136e9066c0503705099a2f4d1ed29ce90b9d54c44802500c68cabb7 | 0.561135 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.05065 | paper | corpus_metadata | null | AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles | Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a n... | https://arxiv.org/abs/1705.05065 | 2017-05-15T04:06:22Z | 1705.05065 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1705.05065 | d6dd9324efca5b83a634ec1699f81d2ab0b8a41930acff10bdbb07bdfdd6b103 | 0.55184 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2001.11285 | paper | corpus_metadata | null | LowResourceEval-2019: a shared task on morphological analysis for low-resource languages | The paper describes the results of the first shared task on morphological analysis for the languages of Russia, namely, Evenki, Karelian, Selkup, and Veps. For the languages in question, only small-sized corpora are available. The tasks include morphological analysis, word form generation and morpheme segmentation. Fou... | https://arxiv.org/abs/2001.11285 | 2020-01-30T12:47:50Z | 2001.11285 | 10.48550/arxiv.2001.11285 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2001.11285 | bf99a9ccdb3ac6770278f455ca89cd5ef8ecbe6850f0ed0b3cc58d80a63ba60a | 0.644644 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.03389 | paper | corpus_metadata | null | A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions | We study how to dynamize the Trapezoidal Search Tree - a well known randomized point location structure for planar subdivisions of kinetic line segments. Our approach naturally extends incremental leaf-level insertions to recursive methods and allows adaptation for the online setting. Moreover, the dynamization carries... | https://arxiv.org/abs/1912.03389 | 2019-12-06T23:30:05Z | 1912.03389 | 10.4230/lipics.icalp.2020.18 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.4230/lipics.icalp.2020.18 | 04b863eda56f2f40e47dec508c514125c320fa09c853509da0f7e99a2a2ceace | 0.508111 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.00593 | paper | corpus_metadata | PointNet | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation | Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directl... | https://arxiv.org/abs/1612.00593 | 2016-12-02T08:40:40Z | 1612.00593 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1612.00593 | f88d168fdeac8634a4d22b51d07db427fbe0b861d303ed4896fe77b55c7c3ac7 | 0.650814 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:3148"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.05587 | paper | corpus_metadata | DeepLabV3 | Rethinking Atrous Convolution for Semantic Image Segmentation | In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scale... | https://arxiv.org/abs/1706.05587 | 2017-06-17T22:48:57Z | 1706.05587 | 10.48550/arxiv.1706.05587 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1706.05587 | 1ae6ec7507ef875459ae10fa889ddc66458abc4befde4a8bcb5d4bc17059e19c | 0.762242 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:3107"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.06476 | paper | corpus_metadata | null | Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction | In VP9 video codec, the sizes of blocks are decided during encoding by recursively partitioning 64$\times$64 superblocks using rate-distortion optimization (RDO). This process is computationally intensive because of the combinatorial search space of possible partitions of a superblock. Here, we propose a deep learning ... | https://arxiv.org/abs/1906.06476 | 2019-06-15T05:50:25Z | 1906.06476 | 10.1109/tip.2020.3011270 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tip.2020.3011270 | 493612daee5afbe6c8cddba7cd3170aa879c7df8250d0b4fb54b31e7ddab3e20 | 0.670537 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.02611 | paper | corpus_metadata | DeepLabV3+ | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation | Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-vie... | https://arxiv.org/abs/1802.02611 | 2018-02-07T19:37:11Z | 1802.02611 | 10.48550/arxiv.1802.02611 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.02611 | 09600da92f176222381b94f1f4091613737b0c9018763f4bc180639ac6873c60 | 0.779952 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:3031"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.01447 | paper | corpus_metadata | null | Transform-Invariant Convolutional Neural Networks for Image Classification and Search | Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matri... | https://arxiv.org/abs/1912.01447 | 2019-11-28T13:09:21Z | 1912.01447 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1912.01447 | ca98537656b1d388ac021975528cc2c4049e3beb2109a626ac14d73c0efbc7b1 | 0.850271 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1607.03250 | paper | corpus_metadata | null | Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures | State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive ... | https://arxiv.org/abs/1607.03250 | 2016-07-12T07:43:01Z | 1607.03250 | 10.48550/arxiv.1607.03250 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1607.03250 | 1a6fd953085b100df23e0ee8c2022717680d5138c379030328d8865bace232c7 | 0.77223 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.10084 | paper | corpus_metadata | null | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair i... | https://arxiv.org/abs/1908.10084 | 2019-08-27T08:50:17Z | 1908.10084 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | arxiv:1908.10084 | 2312e5bdb4bffafc2bb5244e451bf89e2fc1c83063e3108c7e7d5cd16362e41b | 0.773831 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.05418 | paper | corpus_metadata | null | MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition | Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical inform... | https://arxiv.org/abs/2107.05418 | 2021-07-12T13:39:06Z | 2107.05418 | 10.48550/arxiv.2107.05418 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2107.05418 | 3381e1218665a07537bdaf830c73a7d6b61dd8e0e72df8b1f9cf0266d9aea8d5 | 0.671854 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.08933 | paper | corpus_metadata | null | Analyzing Machine Learning Workloads Using a Detailed GPU Simulator | Most deep neural networks deployed today are trained using GPUs via high-level frameworks such as TensorFlow and PyTorch. This paper describes changes we made to the GPGPU-Sim simulator to enable it to run PyTorch by running PTX kernels included in NVIDIA's cuDNN library. We use the resulting modified simulator, which ... | https://arxiv.org/abs/1811.08933 | 2018-11-18T07:52:34Z | 1811.08933 | 10.48550/arxiv.1811.08933 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.08933 | b665e31e375fd6d4ae500f3d949a392e8c0a60393abbe5ba93c217698b325c66 | 0.595097 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.04973 | paper | corpus_metadata | null | Pixel-Wise Motion Deblurring of Thermal Videos | Uncooled microbolometers can enable robots to see in the absence of visible illumination by imaging the "heat" radiated from the scene. Despite this ability to see in the dark, these sensors suffer from significant motion blur. This has limited their application on robotic systems. As described in this paper, this moti... | https://arxiv.org/abs/2006.04973 | 2020-06-08T22:35:12Z | 2006.04973 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2006.04973 | 50f42d674bcd974ee5162b1629ad1027e6a183fbdc5189bd75985bff11ede6d2 | 0.614574 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.08899 | paper | corpus_metadata | null | Limitations of Lazy Training of Two-layers Neural Networks | We study the supervised learning problem under either of the following two models: (1) Feature vectors ${\boldsymbol x}_i$ are $d$-dimensional Gaussians and responses are $y_i = f_*({\boldsymbol x}_i)$ for $f_*$ an unknown quadratic function; (2) Feature vectors ${\boldsymbol x}_i$ are distributed as a mixture of two $... | https://arxiv.org/abs/1906.08899 | 2019-06-21T00:29:54Z | 1906.08899 | 10.48550/arxiv.1906.08899 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.08899 | 3393f91b3e63804cb579372466a333abf48532114bb4f476153035f47df6cc2a | 0.62445 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.08665 | paper | corpus_metadata | null | A multimodal deep learning framework for scalable content based visual media retrieval | We propose a novel, efficient, modular and scalable framework for content based visual media retrieval systems by leveraging the power of Deep Learning which is flexible to work both for images and videos conjointly and we also introduce an efficient comparison and filtering metric for retrieval. We put forward our fin... | https://arxiv.org/abs/2105.08665 | 2021-05-18T16:49:08Z | 2105.08665 | 10.48550/arxiv.2105.08665 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2105.08665 | 84ef5434244973be3ce50491d3e2022dd6ff9ea4a8b886228751934caa72a9cd | 0.766425 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1701.03077 | paper | corpus_metadata | null | A General and Adaptive Robust Loss Function | We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, our loss function allows algorithms built around robust loss minimization to be generalized, which imp... | https://arxiv.org/abs/1701.03077 | 2017-01-11T17:39:14Z | 1701.03077 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1701.03077 | 95019afc667a8708c974e5fc6b3519a42ae508b8cce5631cdb42b26828acbdaa | 0.712958 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.13476 | paper | corpus_metadata | null | CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation | We present a new approach for a single view, image-based object pose estimation. Specifically, the problem of culling false positives among several pose proposal estimates is addressed in this paper. Our proposed approach targets the problem of inaccurate confidence values predicted by CNNs which is used by many curren... | https://arxiv.org/abs/1909.13476 | 2019-09-30T06:39:05Z | 1909.13476 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1909.13476 | bec81f9eec2aeb938381bfe94365eea9ba655ae9ec1c5c58755f921061d4e521 | 0.778781 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.07291 | paper | corpus_metadata | null | Semantic Image Synthesis with Spatially-Adaptive Normalization | We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers.... | https://arxiv.org/abs/1903.07291 | 2019-03-18T08:12:23Z | 1903.07291 | 10.48550/arxiv.1903.07291 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1903.07291 | 34c3532207a7dc3b05ace0b02f43e0684b9e8f6d80a97394e82a8fe20a6c558f | 0.780082 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.00762 | paper | corpus_metadata | null | Flashlight CNN Image Denoising | This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising graysc... | https://arxiv.org/abs/2003.00762 | 2020-03-02T11:09:25Z | 2003.00762 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.00762 | 7b4ef095ee67baff683ddadee9724b396150628577005041b77879817c8dec80 | 0.707233 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.09069 | paper | corpus_metadata | null | First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise | Stochastic gradient descent (SGD) has been widely used in machine learning due to its computational efficiency and favorable generalization properties. Recently, it has been empirically demonstrated that the gradient noise in several deep learning settings admits a non-Gaussian, heavy-tailed behavior. This suggests tha... | https://arxiv.org/abs/1906.09069 | 2019-06-21T11:14:59Z | 1906.09069 | 10.48550/arxiv.1906.09069 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.09069 | c105dd5a30e44a4a78570c73368fa2ed9d485f4f7dac71cca3ad11c9d605e880 | 0.526938 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.04328 | paper | corpus_metadata | null | Importance Resampling for Off-policy Prediction | Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Im... | https://arxiv.org/abs/1906.04328 | 2019-06-11T00:30:17Z | 1906.04328 | 10.48550/arxiv.1906.04328 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.04328 | f1159bbfd3971504e50f906392fd0899b481be26783bf189b89dd54888116719 | 0.52479 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.05656 | paper | corpus_metadata | null | PHYRE: A New Benchmark for Physical Reasoning | Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sa... | https://arxiv.org/abs/1908.05656 | 2019-08-15T17:58:32Z | 1908.05656 | 10.48550/arxiv.1908.05656 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.05656 | 4c532f5a80f865f69a586adf154913ba939a1550cce2b218edf73106fe264358 | 0.658082 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.01442 | paper | corpus_metadata | null | Towards a Zero-One Law for Column Subset Selection | There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-$k$ matrix $B$ minimizing the sum of absolute values of differences to a given $n$-by-$n$ matrix $A$, $\min_{\textrm{rank-}k~B}\|A-B\|_1$, or more generally finding a rank-$k$ matrix $B$ which minimizes... | https://arxiv.org/abs/1811.01442 | 2018-11-04T21:43:55Z | 1811.01442 | 10.48550/arxiv.1811.01442 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1811.01442 | 50bfc4f61477463bae28360f8a9f6ed6d4950c841fc32c4236a79f53229f62a0 | 0.505932 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.01161 | paper | corpus_metadata | null | Embedding Symbolic Knowledge into Deep Networks | In this work, we aim to leverage prior symbolic knowledge to improve the performance of deep models. We propose a graph embedding network that projects propositional formulae (and assignments) onto a manifold via an augmented Graph Convolutional Network (GCN). To generate semantically-faithful embeddings, we develop te... | https://arxiv.org/abs/1909.01161 | 2019-09-03T13:23:25Z | 1909.01161 | 10.48550/arxiv.1909.01161 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.01161 | 3b18611a641a9815df63f6c247bb438262f6d1304338a03b5efc697bd28d8c66 | 0.737969 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.00937 | paper | corpus_metadata | VQ-VAE | Neural Discrete Representation Learning | Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder ne... | https://arxiv.org/abs/1711.00937 | 2017-11-02T21:14:44Z | 1711.00937 | 10.48550/arxiv.1711.00937 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.00937 | a4ec4ecc93189f560585cc47f51c8f8d5dda16d4b4bea7cfcde835d41a219524 | 0.768197 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1906.09323 | paper | corpus_metadata | null | Reinforcement Learning with Convex Constraints | In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints. For instance, the designer may want to limit the use of unsafe actions, increase the diversity of trajectories to enable exploratio... | https://arxiv.org/abs/1906.09323 | 2019-06-21T21:04:27Z | 1906.09323 | 10.48550/arxiv.1906.09323 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.09323 | 1dfb93640947fad7786bb418cb9e3583c83e2e841f988e7053e8705f23440a4c | 0.680922 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1907.08653 | paper | corpus_metadata | null | Surfing: Iterative optimization over incrementally trained deep networks | We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\hat\theta}(x) = \frac{1}{2}\|G_{\hat\theta}(x) - y\|^2$ for certain families of deep networks $G_{\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during diffe... | https://arxiv.org/abs/1907.08653 | 2019-07-19T19:16:04Z | 1907.08653 | 10.48550/arxiv.1907.08653 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1907.08653 | 537a4808adca7781f8b3cc831e8f259e7fc20cd7259cfb8eab71ae717bd818b1 | 0.515816 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.09480 | paper | corpus_metadata | null | A neurally plausible model learns successor representations in partially observable environments | Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are often not directly observable but must be inferred using available sens... | https://arxiv.org/abs/1906.09480 | 2019-06-22T18:05:07Z | 1906.09480 | 10.48550/arxiv.1906.09480 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.09480 | c8819c081fcf233880b69aed227bac9738b0e9b6f7ff17751ed0396ba1133507 | 0.579826 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.06874 | paper | corpus_metadata | null | An Information-theoretic Framework for the Lossy Compression of Link Streams | Graph compression is a data analysis technique that consists in the replacement of parts of a graph by more general structural patterns in order to reduce its description length. It notably provides interesting exploration tools for the study of real, large-scale, and complex graphs which cannot be grasped at first gla... | https://arxiv.org/abs/1807.06874 | 2018-07-18T11:48:37Z | 1807.06874 | 10.48550/arxiv.1807.06874 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.1807.06874 | ea744520e0090b5b354863fa08451d57f3c5bca19d0bca6fcb58104b9ce3ad8b | 0.503251 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.04764 | paper | corpus_metadata | null | Capsules with Inverted Dot-Product Attention Routing | We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces seq... | https://arxiv.org/abs/2002.04764 | 2020-02-12T02:09:33Z | 2002.04764 | 10.48550/arxiv.2002.04764 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.04764 | fb90eef583ec2dc821f53717529d86b7ee0e73da481235af835c64aa1800a26f | 0.763457 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.13038 | paper | corpus_metadata | null | Learning to Predict Without Looking Ahead: World Models Without Forward Prediction | Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware---e.g., a brain---arose as ... | https://arxiv.org/abs/1910.13038 | 2019-10-29T02:17:27Z | 1910.13038 | 10.48550/arxiv.1910.13038 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1910.13038 | cfaef214484cc4b5c1189e430acdfeffccb19f679364b94f6486f8311fc3b368 | 0.75237 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.13678 | paper | corpus_metadata | null | Designing Network Design Spaces | In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks.... | https://arxiv.org/abs/2003.13678 | 2020-03-30T17:57:47Z | 2003.13678 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.13678 | 09f315bb63d7d7ada0b81c90711aa0cd5b6e7b3f74a252986737884b437a41cf | 0.635694 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.11252 | paper | corpus_metadata | null | Stochastic Variational Video Prediction | Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of ... | https://arxiv.org/abs/1710.11252 | 2017-10-30T21:48:54Z | 1710.11252 | 10.48550/arxiv.1710.11252 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1710.11252 | 02ba3de4a248eaeb2b00952dcebc676fc9cb1bb5aebb76c427ef5572eebe3dd6 | 0.714777 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1408.5882 | paper | corpus_metadata | null | Convolutional Neural Networks for Sentence Classification | We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vecto... | https://arxiv.org/abs/1408.5882 | 2014-08-25T19:48:04Z | 1408.5882 | 10.48550/arxiv.1408.5882 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1408.5882 | 1fc2121a9543a7c6d93ea7e6d0d3ef4cc0fd46484cc0cff045e515e38112b8f4 | 0.821859 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.03779 | paper | corpus_metadata | null | k-Space Deep Learning for Accelerated MRI | The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. The success of ALOHA is due to the concise signal representation in the k-space domain thanks to... | https://arxiv.org/abs/1805.03779 | 2018-05-10T01:43:19Z | 1805.03779 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1805.03779 | a9090572aefad16979fcc7eca1364f039c0d4a789c6aca59f8540ef32c777e89 | 0.720327 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.01226 | paper | corpus_metadata | null | Reachability Analysis for Feed-Forward Neural Networks using Face Lattices | Deep neural networks have been widely applied as an effective approach to handle complex and practical problems. However, one of the most fundamental open problems is the lack of formal methods to analyze the safety of their behaviors. To address this challenge, we propose a parallelizable technique to compute exact re... | https://arxiv.org/abs/2003.01226 | 2020-03-02T22:23:57Z | 2003.01226 | 10.48550/arxiv.2003.01226 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.01226 | 04d4666868550b6abe422ccd1236d98641cb0695109b3cd8025f7de3c264be49 | 0.702682 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.05051 | paper | corpus_metadata | null | Deep learning with convolutional neural networks for EEG decoding and visualization | PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full Code available here: https://github.com/robintibor/braindecode | https://arxiv.org/abs/1703.05051 | 2017-03-15T09:52:58Z | 1703.05051 | 10.1002/hbm.23730 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1002/hbm.23730 | a224d1dff509d5ac8211943e24185067c8d577dcc49e3aaf05e3bd3a7807c3e4 | 0.680954 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.10722 | paper | corpus_metadata | null | Factorization tricks for LSTM networks | We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and stat... | https://arxiv.org/abs/1703.10722 | 2017-03-31T00:50:37Z | 1703.10722 | 10.48550/arxiv.1703.10722 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.10722 | c3ce163efe2aadc630739be55f9783976199efe623cdf780a7ca91f357970fd4 | 0.588812 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.00976 | paper | corpus_metadata | null | Contrastive Adaptation Network for Unsupervised Domain Adaptation | Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue... | https://arxiv.org/abs/1901.00976 | 2019-01-04T03:58:10Z | 1901.00976 | 10.48550/arxiv.1901.00976 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.00976 | 1794b52c4dfe98377ccba9e23a579cc70123039535956e2faa7641489d67a18b | 0.802169 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.03463 | paper | corpus_metadata | null | Learning to Generalize: Meta-Learning for Domain Generalization | Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We prop... | https://arxiv.org/abs/1710.03463 | 2017-10-10T09:15:16Z | 1710.03463 | 10.48550/arxiv.1710.03463 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1710.03463 | e62914091c4a87d5b1641a40ac695939c1756a80b4d6af6817b31baea7aacaef | 0.852106 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.03955 | paper | corpus_metadata | null | Kervolutional Neural Networks | Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve th... | https://arxiv.org/abs/1904.03955 | 2019-04-08T11:10:51Z | 1904.03955 | 10.1109/cvpr.2019.00012 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/cvpr.2019.00012 | a5fd190c1f05aba6dbce75b8c1ec34e5f1559cd28ccaf3ec692d334f42c2bca9 | 0.747608 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.12852 | paper | corpus_metadata | null | Probably Approximately Correct Vision-Based Planning using Motion Primitives | This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all environments. Minimizin... | https://arxiv.org/abs/2002.12852 | 2020-02-28T16:29:59Z | 2002.12852 | 10.48550/arxiv.2002.12852 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.12852 | d14b260baddfd57147c831f180c378b7e102281210b459ae306d25452c2ea594 | 0.629186 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.10522 | paper | corpus_metadata | null | Practical calibration of the temperature parameter in Gibbs posteriors | PAC-Bayesian algorithms and Gibbs posteriors are gaining popularity due to their robustness against model misspecification even when Bayesian inference is inconsistent. The PAC-Bayesian alpha-posterior is a generalization of the standard Bayes posterior which can be tempered with a parameter alpha to handle inconsisten... | https://arxiv.org/abs/2004.10522 | 2020-04-22T12:23:45Z | 2004.10522 | 10.48550/arxiv.2004.10522 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.10522 | 79a3197199d26214e7a1d92f9b6b47ac47164f407dfbc38fc4ca16b39ee3de56 | 0.609658 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.11162 | paper | corpus_metadata | null | Constraint Answer Set Programming without Grounding | Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this... | https://arxiv.org/abs/1804.11162 | 2018-04-30T12:50:28Z | 1804.11162 | 10.1017/s1471068418000285 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1017/s1471068418000285 | 0ddddebbb1d3fed00abd9017c5f06706d8fb157337993f983f9fe5697b3377e7 | 0.596201 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.07269 | paper | corpus_metadata | null | Exploring Modern GPU Memory System Design Challenges through Accurate Modeling | This paper explores the impact of simulator accuracy on architecture design decisions in the general-purpose graphics processing unit (GPGPU) space. We perform a detailed, quantitative analysis of the most popular publicly available GPU simulator, GPGPU-Sim, against our enhanced version of the simulator, updated to mod... | https://arxiv.org/abs/1810.07269 | 2018-10-16T20:52:59Z | 1810.07269 | 10.1109/isca45697.2020.00047 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/isca45697.2020.00047 | d1480d12751799f29b7f78cdc6d631312788e266e3db7b7457e77a42c96f2c2a | 0.590873 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.08040 | paper | corpus_metadata | null | A Simple Exponential Family Framework for Zero-Shot Learning | We present a simple generative framework for learning to predict previously unseen classes, based on estimating class-attribute-gated class-conditional distributions. We model each class-conditional distribution as an exponential family distribution and the parameters of the distribution of each seen/unseen class are d... | https://arxiv.org/abs/1707.08040 | 2017-07-25T15:28:22Z | 1707.08040 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/publicdomain/zero/1.0 | arxiv:1707.08040 | 481593af4a8829f650276f02aec58d1d6d40937963d7b733cb206012faea1620 | 0.722161 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1601.01356 | paper | corpus_metadata | null | From Word Embeddings to Item Recommendation | Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techn... | https://arxiv.org/abs/1601.01356 | 2016-01-07T00:09:37Z | 1601.01356 | 10.48550/arxiv.1601.01356 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1601.01356 | 2301c46041112bd57c8082eee231816b3a9c6c3c51e061103765e1446d081756 | 0.670712 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.07906 | paper | corpus_metadata | null | PCRNet: Point Cloud Registration Network using PointNet Encoding | PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion. However, recent works in literature have shown the sensitivity of the PointNet representation to pose misalignment. Thi... | https://arxiv.org/abs/1908.07906 | 2019-08-21T15:10:24Z | 1908.07906 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1908.07906 | 3f4a99669909e6b3ac41e010aeaf27c37d736822e42494c291e8a01371ca95e8 | 0.727371 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.13962 | paper | corpus_metadata | null | NViSII: A Scriptable Tool for Photorealistic Image Generation | We present a Python-based renderer built on NVIDIA's OptiX ray tracing engine and the OptiX AI denoiser, designed to generate high-quality synthetic images for research in computer vision and deep learning. Our tool enables the description and manipulation of complex dynamic 3D scenes containing object meshes, material... | https://arxiv.org/abs/2105.13962 | 2021-05-28T16:35:32Z | 2105.13962 | 10.48550/arxiv.2105.13962 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.13962 | 45e75eb9fbb8f4bf5937c38b2961ff4865ecc0cd3fd6990bd21e8aa764968322 | 0.676607 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1606.01781 | paper | corpus_metadata | VDCNN (on Amazon Review Full dataset) | Very Deep Convolutional Networks for Text Classification | The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN... | https://arxiv.org/abs/1606.01781 | 2016-06-06T15:14:50Z | 1606.01781 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1606.01781 | 646fabda52d5f6d99e7313c64a5572799d92aba04b261c2143931d7d0e6fd5a0 | 0.801075 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:3128"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.05796 | paper | corpus_metadata | null | Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision | Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of CLIP, reproducing these works remains challenging. This is because researchers do not choose consistent training recipes and even use differe... | https://arxiv.org/abs/2203.05796 | 2022-03-11T08:41:00Z | 2203.05796 | 10.48550/arxiv.2203.05796 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2203.05796 | 1a2deb621c3d3bc015233ae08d31845714aa780bb3beb407c5cb7287145cc2fb | 0.841092 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
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