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arxiv:2609.28923 | paper | corpus_metadata | null | ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation | null | https://arxiv.org/abs/2609.28923 | 2026-09-24 | 2609.28923 | 10.48550/arxiv.2609.28923 | gh-ml-hf-daily-papers | 312d114b81224ca049577eb319ac1434a828aed1 | CC0 | null | doi:10.48550/arxiv.2609.28923 | bf0170170609c565ab35dc39517175c0da709ebfdddd3b2b8d2923e3a16bc437 | 0.512027 | scored | title_only | true | true | en | 0.995289 | eligible | classifier | true | [
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arxiv:2609.29028 | paper | corpus_metadata | null | RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation | null | https://arxiv.org/abs/2609.29028 | 2026-09-24 | 2609.29028 | 10.48550/arxiv.2609.29028 | gh-ml-hf-daily-papers | 312d114b81224ca049577eb319ac1434a828aed1 | CC0 | null | doi:10.48550/arxiv.2609.29028 | 7c87e78669fb9cadcf04b5944e497c3bc560b214fe981e7f4873b1d58746f710 | 0.590776 | scored | title_only | true | true | en | 0.974896 | eligible | classifier | true | [
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arxiv:2609.29429 | paper | corpus_metadata | null | Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures | null | https://arxiv.org/abs/2609.29429 | 2026-09-24 | 2609.29429 | 10.48550/arxiv.2609.29429 | gh-ml-hf-daily-papers | 312d114b81224ca049577eb319ac1434a828aed1 | CC0 | null | doi:10.48550/arxiv.2609.29429 | ecb79c86d52e75bbd15ed6b93587b528748cdd9a415fa71e7d52d920ddd6d4e6 | 0.519535 | scored | title_only | true | true | en | 1 | eligible | classifier | true | [
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arxiv:2609.29816 | paper | corpus_metadata | null | AV-GRPO: Modality-Anchored Decoupling Diffusion Reinforcement Learning for Joint Audio-Video Generation | null | https://arxiv.org/abs/2609.29816 | 2026-09-24 | 2609.29816 | 10.48550/arxiv.2609.29816 | gh-ml-hf-daily-papers | 312d114b81224ca049577eb319ac1434a828aed1 | CC0 | null | doi:10.48550/arxiv.2609.29816 | 713c0797e2405344432a82438bdea45350285f3403e8c8541e5cfe87c26c2e53 | 0.515509 | scored | title_only | true | true | en | 0.99569 | eligible | classifier | true | [
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arxiv:1810.10559 | paper | corpus_metadata | null | Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models | Cross-validation can be used to measure a model's predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV) requires that the observation model can be factorized into simple terms, but a lot of important models in temporal and spatial statistics d... | https://arxiv.org/abs/1810.10559 | 2018-10-24T18:03:36Z | 1810.10559 | 10.1007/s00180-020-01045-4 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s00180-020-01045-4 | 77ce35969abe64bd0e1f14686c3526f6464b52a98e24c8d82a4ebb68bc7d2ca0 | 0.528889 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.10485 | paper | corpus_metadata | null | AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying at... | https://arxiv.org/abs/1711.10485 | 2017-11-28T18:59:50Z | 1711.10485 | 10.48550/arxiv.1711.10485 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.10485 | 47a4c50823453920735c1eb43f208a350141dc20f17c3f1462b528e5279c4e1c | 0.805917 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.08393 | paper | corpus_metadata | null | Distilling Interpretable Models into Human-Readable Code | The goal of model distillation is to faithfully transfer teacher model knowledge to a model which is faster, more generalizable, more interpretable, or possesses other desirable characteristics. Human-readability is an important and desirable standard for machine-learned model interpretability. Readable models are tran... | https://arxiv.org/abs/2101.08393 | 2021-01-21T01:46:36Z | 2101.08393 | 10.48550/arxiv.2101.08393 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2101.08393 | 05be88b1e13c5a09393e011f36c17b66de8e6e736418551e2eca87deed0cee20 | 0.747815 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.07146 | paper | corpus_metadata | Wide Residual Network | Wide Residual Networks | Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes t... | https://arxiv.org/abs/1605.07146 | 2016-05-23T19:27:13Z | 1605.07146 | 10.48550/arxiv.1605.07146 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.07146 | fd3de9846d3bf54f356a86963151b9eb58669209204e1f2dc090b4a6a6dc5841 | 0.79743 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1609.06647 | paper | corpus_metadata | null | Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge | Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation... | https://arxiv.org/abs/1609.06647 | 2016-09-21T17:40:57Z | 1609.06647 | 10.1109/tpami.2016.2587640 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tpami.2016.2587640 | 5decf4ce676f52c2345d6495fc65cd76e2ded89fe1b5cc065a260fc39bdb711f | 0.791097 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.07162 | paper | corpus_metadata | null | A Wavenet for Speech Denoising | Currently, most speech processing techniques use magnitude spectrograms as front-end and are therefore by default discarding part of the signal: the phase. In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet. The proposed model adaptation retains Wavenet'... | https://arxiv.org/abs/1706.07162 | 2017-06-22T04:13:43Z | 1706.07162 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:1706.07162 | 4cae8ca859df9986400a9dbbe260d5f7cc513d268ae3c2515e608b1ed1f33768 | 0.716779 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.10916 | paper | corpus_metadata | null | StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks | Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-... | https://arxiv.org/abs/1710.10916 | 2017-10-19T18:45:59Z | 1710.10916 | 10.1109/tpami.2018.2856256 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tpami.2018.2856256 | 2638ea1baed9a0daada224259aeb6048340039fafb03f45f8812086c8dfe4af7 | 0.759531 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1610.04794 | paper | corpus_metadata | null | Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering | Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transfo... | https://arxiv.org/abs/1610.04794 | 2016-10-15T22:51:06Z | 1610.04794 | 10.48550/arxiv.1610.04794 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1610.04794 | a7dad8f995c127330b4032dae041fba564852ac5574531a961ec481862a259bb | 0.712579 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.03705 | paper | corpus_metadata | null | Online Deep Learning: Learning Deep Neural Networks on the Fly | Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open ch... | https://arxiv.org/abs/1711.03705 | 2017-11-10T05:54:14Z | 1711.03705 | 10.48550/arxiv.1711.03705 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.03705 | 6d9705418981910df511efbf167a17e55c088e15c44e5d361d3cf05634323781 | 0.81586 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.05884 | paper | corpus_metadata | null | Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection | Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labe... | https://arxiv.org/abs/1809.05884 | 2018-09-16T14:35:03Z | 1809.05884 | 10.1145/3240508.3240567 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3240508.3240567 | 0fb13d7025f0f05d5a106470fbdf937eae2311211e49fb6d2fa60daaa5942d42 | 0.841022 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.04043 | paper | corpus_metadata | null | Few-Shot Learning with Graph Neural Networks | We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we defin... | https://arxiv.org/abs/1711.04043 | 2017-11-10T23:32:47Z | 1711.04043 | 10.48550/arxiv.1711.04043 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.04043 | 9d351b4ae35bed57accc0cb48f95490f8cf2068c9f2ef4c368eeb638aef5f2b3 | 0.782113 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1406.2661 | paper | corpus_metadata | null | Generative Adversarial Networks | We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training... | https://arxiv.org/abs/1406.2661 | 2014-06-10T18:58:17Z | 1406.2661 | 10.48550/arxiv.1406.2661 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1406.2661 | e8de99c0ba6b21e2b3a57e5b05f2f94b91d18b1310a4d313eed791d7737db8a9 | 0.760723 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1712.01815 | paper | corpus_metadata | AlphaZero | Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm | The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast... | https://arxiv.org/abs/1712.01815 | 2017-12-05T18:45:38Z | 1712.01815 | 10.48550/arxiv.1712.01815 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1712.01815 | ddc009016a84ff9bdc7ae79a2bb0f47bd38f7cc13c8a4f57d1a26ec25a3fe04b | 0.58764 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1708.02002 | paper | corpus_metadata | RetinaNet-R50 | Focal Loss for Dense Object Detection | The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster... | https://arxiv.org/abs/1708.02002 | 2017-08-07T06:32:42Z | 1708.02002 | 10.48550/arxiv.1708.02002 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.02002 | 06135b73ecb9b8d2860c99ef6f439fd746783f96597804c7c6e2cf9161246540 | 0.677752 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:3092",
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arxiv:1507.02672 | paper | corpus_metadata | null | Semi-Supervised Learning with Ladder Networks | We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (... | https://arxiv.org/abs/1507.02672 | 2015-07-09T19:52:19Z | 1507.02672 | 10.48550/arxiv.1507.02672 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1507.02672 | dfd18443533ebe9b99e5f17ce6210e3f99d1cc31313ada9f52f27eb028c9d4e9 | 0.7992 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.06906 | paper | corpus_metadata | null | Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search | While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few ... | https://arxiv.org/abs/1807.06906 | 2018-07-18T13:11:08Z | 1807.06906 | 10.48550/arxiv.1807.06906 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.06906 | 82eaf2f63611aa5e07c42bd3f86c07695047d833e1c4a975f8d6d2195123099b | 0.599719 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.01670 | paper | corpus_metadata | null | Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework | In this paper, we aim at improving the performance of synthesized speech in statistical parametric speech synthesis (SPSS) based on a generative adversarial network (GAN). In particular, we propose a novel architecture combining the traditional acoustic loss function and the GAN's discriminative loss under a multi-task... | https://arxiv.org/abs/1707.01670 | 2017-07-06T08:01:24Z | 1707.01670 | 10.48550/arxiv.1707.01670 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1707.01670 | c527032a1a18ee16666cacc3eb218326c4970e612186d7a27ca2151847b51cc8 | 0.72473 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1609.05158 | paper | corpus_metadata | null | Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network | Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly... | https://arxiv.org/abs/1609.05158 | 2016-09-16T17:58:14Z | 1609.05158 | 10.48550/arxiv.1609.05158 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1609.05158 | a567ce8567ad4ccc998cf19b69c43985a17ae00aaaec6319cce044a4e6c3ab98 | 0.804222 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1409.2903 | paper | corpus_metadata | null | A structured matrix factorization framework for large scale calcium imaging data analysis | We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity of each neuron from the slow dynamics of... | https://arxiv.org/abs/1409.2903 | 2014-09-09T21:25:59Z | 1409.2903 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1409.2903 | 54d7be0311c04c12b8f08b4328baf3096994a1614b919b130a547926e7d76a37 | 0.519541 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.02709 | paper | corpus_metadata | null | Recent Trends in Deep Learning Based Natural Language Processing | Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep... | https://arxiv.org/abs/1708.02709 | 2017-08-09T04:02:17Z | 1708.02709 | 10.48550/arxiv.1708.02709 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.1708.02709 | ba63ee805e0bcc5e3c814b90b6135e246d03e4997d4f6450c22160545f293763 | 0.727666 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.10155 | paper | corpus_metadata | null | CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training | We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in fine-grained categories, such as faces of a specific person or objects in a category. Our approach models an image as a composit... | https://arxiv.org/abs/1703.10155 | 2017-03-29T17:49:48Z | 1703.10155 | 10.48550/arxiv.1703.10155 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.10155 | 28af7c15a141d6cd22231c66ac4d1270b907110ec4ce48a8b1867f950cb6f612 | 0.818346 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1406.2541 | paper | corpus_metadata | null | Predictive Entropy Search for Efficient Global Optimization of Black-box Functions | We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of t... | https://arxiv.org/abs/1406.2541 | 2014-06-10T13:29:09Z | 1406.2541 | 10.48550/arxiv.1406.2541 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1406.2541 | 036a6fda600d8ac0e611b0d0cbc62361aef05335f9b1415bb0829d20b8375fd9 | 0.594511 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.01703 | paper | corpus_metadata | null | Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning | In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tags, such as ImageNet. However, a single tag cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work,... | https://arxiv.org/abs/1901.01703 | 2019-01-07T08:35:15Z | 1901.01703 | 10.1109/access.2019.2956775 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/access.2019.2956775 | de494a78ddebb3f788006a450f0f4cc13e3d4f749a8ac610aa506914c98d8d06 | 0.819538 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.07798 | paper | corpus_metadata | null | Visual Relationship Detection with Language prior and Softmax | Visual relationship detection is an intermediate image understanding task that detects two objects and classifies a predicate that explains the relationship between two objects in an image. The three components are linguistically and visually correlated (e.g. "wear" is related to "person" and "shirt", while "laptop" is... | https://arxiv.org/abs/1904.07798 | 2019-04-16T16:29:52Z | 1904.07798 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1904.07798 | bf7b2408e6d87d2e06b9dabe8c7e27eedc88968922b273c5909a39458bea2fcd | 0.621602 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1503.08895 | paper | corpus_metadata | null | End-To-End Memory Networks | We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generall... | https://arxiv.org/abs/1503.08895 | 2015-03-31T03:05:37Z | 1503.08895 | 10.48550/arxiv.1503.08895 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1503.08895 | ea1cb11068f9e3381d6655e2f7b09352842333a64a7ab8daee4b4a23653f6ee7 | 0.839864 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.05830 | paper | corpus_metadata | null | Towards High Performance Video Object Detection for Mobiles | Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weig... | https://arxiv.org/abs/1804.05830 | 2018-04-16T17:59:36Z | 1804.05830 | 10.48550/arxiv.1804.05830 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.05830 | bf39c778994e758d36bc7262e257d3a970ce242d7283655424cd67b0266a149d | 0.602712 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.08424 | paper | corpus_metadata | null | Multimodal Word Distributions | Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective... | https://arxiv.org/abs/1704.08424 | 2017-04-27T03:59:54Z | 1704.08424 | 10.48550/arxiv.1704.08424 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.08424 | ee940ceb353615b4f2fc4390e90f667a3836749e1ff5da69793385d35de39c76 | 0.648314 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.07567 | paper | corpus_metadata | null | Feature Importance Measure for Non-linear Learning Algorithms | Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientif... | https://arxiv.org/abs/1611.07567 | 2016-11-22T22:36:31Z | 1611.07567 | 10.48550/arxiv.1611.07567 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.07567 | ba94c0f4f8be16dd0f8fcc8e725c9f8303091407982859565d99baf1d045b9a1 | 0.638064 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.03457 | paper | corpus_metadata | null | Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models | Recent advances in data collection and storage have allowed both researchers and industry alike to collect data in real time. Much of this data comes in the form of 'events', or timestamped interactions, such as email and social media posts, website clickstreams, or protein-protein interactions. This of type data poses... | https://arxiv.org/abs/1809.03457 | 2018-09-10T16:53:12Z | 1809.03457 | 10.1142/s0219525919500061 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1142/s0219525919500061 | 5035b5025f97d4be37e621a2c114f91afe7f409c93c5a716491ec0583294079b | 0.552431 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.09433 | paper | corpus_metadata | null | Separating the signal from the noise: Evidence for deceleration in old-age death rates | Widespread population aging has made it critical to understand death rates at old ages. However, studying mortality at old ages is challenging because the data are sparse: numbers of survivors and deaths get smaller and smaller with age. We show how to address this challenge by using principled model selection techniqu... | https://arxiv.org/abs/1707.09433 | 2017-07-28T22:43:42Z | 1707.09433 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1707.09433 | 8e18fcbef5d4f238134c5a3b415c5e42d30bfb656953c45920be5b579fc9e376 | 0.537779 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.09019 | paper | corpus_metadata | null | CNN+CNN: Convolutional Decoders for Image Captioning | Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field. Ho... | https://arxiv.org/abs/1805.09019 | 2018-05-23T09:16:59Z | 1805.09019 | 10.48550/arxiv.1805.09019 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1805.09019 | ca17fdc89e96ab5bcd2a39a86fdc48b3d0ad96a56414a6ff2b91150509cb9fb2 | 0.785535 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.06094 | paper | corpus_metadata | null | SPNets: Differentiable Fluid Dynamics for Deep Neural Networks | In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with ... | https://arxiv.org/abs/1806.06094 | 2018-06-15T18:58:16Z | 1806.06094 | 10.48550/arxiv.1806.06094 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1806.06094 | af3ba037f933ce943387ed1689829bebea216593ea7b11ff75402dee00e0ecc4 | 0.607468 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1301.3781 | paper | corpus_metadata | Efficient Estimation of Word Representations in Vector Space (2013)**, Mikolov, Chen, Corrado, and Dean, + 💽.
#### Neural Network Components
##### Autograd | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | https://arxiv.org/abs/1301.3781 | 2013-01-16T18:24:43Z | 1301.3781 | 10.48550/arxiv.1301.3781 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1301.3781 | d009aca72937f25acda6da111e385859ddf413632e5e54aacf05b37b071cf083 | 0.813666 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"daturkel-landmark-ml-papers",
"epoch-all-ai-models"
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"daturkel-landmark-ml-papers:line:238:url:a9720eb4a51a",
"epoch-all-ai-models:row:3341"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.03130 | paper | corpus_metadata | null | A Structured Self-attentive Sentence Embedding | This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special... | https://arxiv.org/abs/1703.03130 | 2017-03-09T04:42:30Z | 1703.03130 | 10.48550/arxiv.1703.03130 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.03130 | 777e1b9b78401e109f607aeb02db80b5715fb5c1fbd89a2b26405505a9dfb297 | 0.655288 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.08242 | paper | corpus_metadata | YOLOv2 | YOLO9000: Better, Faster, Stronger | We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC ... | https://arxiv.org/abs/1612.08242 | 2016-12-25T07:21:38Z | 1612.08242 | 10.48550/arxiv.1612.08242 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1612.08242 | e6ae5e37bb315e122d2cd02f54199d645457cb521dba109844c1f4532d68aa77 | 0.752037 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:3133"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1505.04597 | paper | corpus_metadata | U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)**, Ronneberger, Fischer, Brox, 🔒 / 🔑.
##### VGG (image recognition CNN) | U-Net: Convolutional Networks for Biomedical Image Segmentation | There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contrac... | https://arxiv.org/abs/1505.04597 | 2015-05-18T11:28:37Z | 1505.04597 | 10.48550/arxiv.1505.04597 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1505.04597 | 9084cc4832b8ebacbab2f0ac9270415ec27f38a03de5f700e41fed5808d025b4 | 0.758062 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"daturkel-landmark-ml-papers"
] | [
"daturkel-landmark-ml-papers:line:159:url:fcf4ca3d2581"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1608.03983 | paper | corpus_metadata | null | SGDR: Stochastic Gradient Descent with Warm Restarts | Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple wa... | https://arxiv.org/abs/1608.03983 | 2016-08-13T13:46:05Z | 1608.03983 | 10.48550/arxiv.1608.03983 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1608.03983 | 56444a8a58c9150c4109c722eb56c465f9d20f0c097ba6e954b91f83a01948f4 | 0.748216 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.01887 | paper | corpus_metadata | null | Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning | Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as "the" and "of". Other words ... | https://arxiv.org/abs/1612.01887 | 2016-12-06T16:03:50Z | 1612.01887 | 10.48550/arxiv.1612.01887 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1612.01887 | a78e12c05238af162d6dc37fa7d417ff91c002ab1ef780bb80847abf88b07634 | 0.765833 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.02364 | paper | corpus_metadata | null | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have no... | https://arxiv.org/abs/1705.02364 | 2017-05-05T18:54:39Z | 1705.02364 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1705.02364 | 0ec84891b4b345db5dbbc370bd55aca8509be252840a395f0fff7884f13c1c63 | 0.837986 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.05861 | paper | corpus_metadata | null | The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents | To achieve social interactions within Human-Robot Interaction (HRI) environments is a very challenging task. Most of the current research focuses on Wizard-of-Oz approaches, which neglect the recent development of intelligent robots. On the other hand, real-world scenarios usually do not provide the necessary control a... | https://arxiv.org/abs/2003.05861 | 2020-03-12T15:52:49Z | 2003.05861 | 10.48550/arxiv.2003.05861 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2003.05861 | 79a1d559e8432d50d96f50ea43d9aa8f8b477f964b808fc7a88f8b52a0e55826 | 0.633466 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.08387 | paper | corpus_metadata | null | Deep Video Deblurring | Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning... | https://arxiv.org/abs/1611.08387 | 2016-11-25T08:51:51Z | 1611.08387 | 10.48550/arxiv.1611.08387 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.08387 | 757f8cd19a14ce4fe114f6f4264b24911e2e495ea09bcfcea282397148e1c362 | 0.763977 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.00148 | paper | corpus_metadata | null | Rethinking on Multi-Stage Networks for Human Pose Estimation | Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods'... | https://arxiv.org/abs/1901.00148 | 2019-01-01T12:52:37Z | 1901.00148 | 10.48550/arxiv.1901.00148 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.00148 | e06f9960f7b3c4c362474b39377e1a3157e367f7196d236c356049d77c794389 | 0.720916 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.10034 | paper | corpus_metadata | null | Semantic Visual Navigation by Watching YouTube Videos | Semantic cues and statistical regularities in real-world environment layouts can improve efficiency for navigation in novel environments. This paper learns and leverages such semantic cues for navigating to objects of interest in novel environments, by simply watching YouTube videos. This is challenging because YouTube... | https://arxiv.org/abs/2006.10034 | 2020-06-17T17:56:00Z | 2006.10034 | 10.48550/arxiv.2006.10034 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.10034 | 52154472335f1127bb882bcd643b4725f06477ea492006b92163fd27306ce5bc | 0.663571 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.04715 | paper | corpus_metadata | null | Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data | Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data which contains the onset and offset annotations of sound events. However, many audio tagging datasets are weakly labelled, that is, only the presence of ... | https://arxiv.org/abs/1804.04715 | 2018-04-12T20:20:29Z | 1804.04715 | 10.1109/taslp.2019.2895254 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/taslp.2019.2895254 | d19067029dc5f6a3b369374ba2d707828d5d143ab0e0daf69e9d4158bbaaa159 | 0.677609 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1511.06581 | paper | corpus_metadata | Dueling DQN | Dueling Network Architectures for Deep Reinforcement Learning | In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement lear... | https://arxiv.org/abs/1511.06581 | 2015-11-20T13:07:54Z | 1511.06581 | 10.48550/arxiv.1511.06581 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1511.06581 | 9b6447a0eff073f88c7865c7dbd43383966c72302e3efa6776258e4d6c680c75 | 0.795213 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1409.1556 | paper | corpus_metadata | Very Deep Convolutional Networks for Large-Scale Image Recognition (2015)**, Simonyan and Zisserman, + 🌐 + 📊 + 🎥.
#### Ensemble Methods
##### AdaBoost | Very Deep Convolutional Networks for Large-Scale Image Recognition | In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improveme... | https://arxiv.org/abs/1409.1556 | 2014-09-04T19:48:04Z | 1409.1556 | 10.48550/arxiv.1409.1556 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1409.1556 | 887d2cf22dbb132574782636bc069023575978952228efe9e2aa1d5fd1e1d0a0 | 0.791833 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"daturkel-landmark-ml-papers",
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"daturkel-landmark-ml-papers:line:163:url:1bb5dbf4e03e",
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"epoch-all-ai-models:row:3290",
"spdin-deep-learning-chronology:line:111"
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arxiv:1910.01592 | paper | corpus_metadata | null | Efficient training of energy-based models via spin-glass control | We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and th... | https://arxiv.org/abs/1910.01592 | 2019-10-03T16:51:26Z | 1910.01592 | 10.1088/2632-2153/abe807 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1088/2632-2153/abe807 | 0efb98be763c164bd144be4763bd37b09b7b8ca70f86aa5cb26753b249c0f56e | 0.766392 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1412.1820 | paper | corpus_metadata | null | Context-Dependent Fine-Grained Entity Type Tagging | Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and Weld, 2012) suggests that using a large fine-grained label set can lead to dramatic improvements in downstream tasks. In the absence of lab... | https://arxiv.org/abs/1412.1820 | 2014-12-03T23:26:33Z | 1412.1820 | 10.48550/arxiv.1412.1820 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1412.1820 | 2d7fea62792e0e732ad019b8771cf297a436df2006e4fd1cf3b9c39508a13e60 | 0.656635 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1609.04468 | paper | corpus_metadata | null | Sampling Generative Networks | We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model's prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifol... | https://arxiv.org/abs/1609.04468 | 2016-09-14T22:42:23Z | 1609.04468 | 10.48550/arxiv.1609.04468 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1609.04468 | c12b892e9e10872e322c68483e43461418e5f12b348a52005915b347f4b966cb | 0.591555 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.07197 | paper | corpus_metadata | null | Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search | Neural architecture search (NAS) has become an important approach to automatically find effective architectures. To cover all possible good architectures, we need to search in an extremely large search space with billions of candidate architectures. More critically, given a large search space, we may face a very challe... | https://arxiv.org/abs/2007.07197 | 2020-07-07T02:29:06Z | 2007.07197 | 10.48550/arxiv.2007.07197 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.07197 | 00299f71aa6da18d55f49991f32b133686fdd2e0ff338f841a88bd41e4e26f69 | 0.698747 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.07413 | paper | corpus_metadata | null | Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss | Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-awa... | https://arxiv.org/abs/1906.07413 | 2019-06-18T07:21:18Z | 1906.07413 | 10.48550/arxiv.1906.07413 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.07413 | bd88c7687920110fb9dca7c710bf50ac7303c60b1703168de645e06a46679813 | 0.791713 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.03590 | paper | corpus_metadata | null | Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language | We address the problem of retrieving a specific moment from an untrimmed video by a query sentence. This is a challenging problem because a target moment may take place in relations to other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they consider temporal moments ... | https://arxiv.org/abs/1912.03590 | 2019-12-08T01:34:39Z | 1912.03590 | 10.48550/arxiv.1912.03590 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1912.03590 | 837f3c65ef1e228b91f3d829b1cfe3887fa2490c40078de26fbe75e6525b34b6 | 0.735023 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.01693 | paper | corpus_metadata | null | Multigrid Predictive Filter Flow for Unsupervised Learning on Videos | We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos. The mgPFF takes as input a pair of frames and outputs per-pixel filters to warp one frame to the other. Compared to optical flow used for warping frames, mgPFF is more powerful in modeling sub-pixel movement and deal... | https://arxiv.org/abs/1904.01693 | 2019-04-02T22:41:48Z | 1904.01693 | 10.48550/arxiv.1904.01693 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1904.01693 | 40b74e24f16aa7f82a708eeb6756a240e329ad7773938d6c6bdf15514beb3d17 | 0.733259 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.11482 | paper | corpus_metadata | null | Image Reconstruction with Predictive Filter Flow | We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution. Given a corrupted input image, the model synthesizes a spatially varying linear filter which, when applied to the input image, reconstructs the desired output. The model parameters... | https://arxiv.org/abs/1811.11482 | 2018-11-28T10:17:14Z | 1811.11482 | 10.48550/arxiv.1811.11482 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.11482 | ea8b86e74d02878064f474a3b0d9aafc6565558fa478e8ac6c4fe4c1daed56aa | 0.783602 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.02968 | paper | corpus_metadata | JFT | Revisiting Unreasonable Effectiveness of Data in Deep Learning Era | The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the s... | https://arxiv.org/abs/1707.02968 | 2017-07-10T17:54:31Z | 1707.02968 | 10.48550/arxiv.1707.02968 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1707.02968 | bc467650e2da4662f9d7624fb00ddd460121fe4c2247cabc88b5a2c4429641a4 | 0.824271 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:3103"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2507.06654 | paper | corpus_metadata | null | MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval | Result diversification (RD) is a crucial technique in Text-to-Image Retrieval for enhancing the efficiency of a practical application. Conventional methods focus solely on increasing the diversity metric of image appearances. However, the diversity metric and its desired value vary depending on the application, which l... | https://arxiv.org/abs/2507.06654 | 2025-07-09T08:38:46Z | 2507.06654 | 10.48550/arxiv.2507.06654 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2507.06654 | 59ec7d72ac80267d3fa9d7ce7c50bc597980206855e626d8ef3f6aa917d21fb3 | 0.719952 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.10432 | paper | corpus_metadata | null | Atlas: End-to-End 3D Scene Reconstruction from Posed Images | We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene. We hypothesize... | https://arxiv.org/abs/2003.10432 | 2020-03-23T17:59:15Z | 2003.10432 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.10432 | df2cc9734e5664e40b5cf1fe0887c7c578d505a55b848a9ea6693007301bd686 | 0.694528 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.08395 | paper | corpus_metadata | null | NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot | Humanoid robotics research depends on capable robot platforms, but recently developed advanced platforms are often not available to other research groups, expensive, dangerous to operate, or closed-source. The lack of available platforms forces researchers to work with smaller robots, which have less strict dynamic con... | https://arxiv.org/abs/1810.08395 | 2018-10-19T08:29:25Z | 1810.08395 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1810.08395 | 455a65fa912119854ae29370674f60f6e075ede3b3d35b79c721125b3abb272e | 0.567441 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.04456 | paper | corpus_metadata | null | Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing | Most of current studies on human gaze and saliency modeling have used high-quality stimuli. In real world, however, captured images undergo various types of distortions during the whole acquisition, transmission, and displaying chain. Some distortion types include motion blur, lighting variations and rotation. Despite ... | https://arxiv.org/abs/1810.04456 | 2018-10-10T11:10:28Z | 1810.04456 | 10.48550/arxiv.1810.04456 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1810.04456 | c6ee1bfc946f20173e48a7751b8da437230400f1051e6c774009bc7fcb277b31 | 0.581857 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.10593 | paper | corpus_metadata | null | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an im... | https://arxiv.org/abs/1703.10593 | 2017-03-30T17:44:17Z | 1703.10593 | 10.48550/arxiv.1703.10593 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.10593 | c9746bfdd32898570635303be93ac7975e9b4336d14e001b6dde4ae314895ef7 | 0.764732 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.05193 | paper | corpus_metadata | null | Bayesian optimization of hyper-parameters in reservoir computing | We describe a method for searching the optimal hyper-parameters in reservoir computing, which consists of a Gaussian process with Bayesian optimization. It provides an alternative to other frequently used optimization methods such as grid, random, or manual search. In addition to a set of optimal hyper-parameters, the ... | https://arxiv.org/abs/1611.05193 | 2016-11-16T09:25:17Z | 1611.05193 | 10.48550/arxiv.1611.05193 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.05193 | c45b1e86651c9afeb73a2ef7cb10513e53d98b49cb0b483c12ddaf296005e33e | 0.594235 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.05593 | paper | corpus_metadata | null | Convolutional neural network architecture for geometric matching | We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric ma... | https://arxiv.org/abs/1703.05593 | 2017-03-16T13:03:54Z | 1703.05593 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1703.05593 | 6626e8b550703b594b82228c1cf5e026eaec803b280b814e2bbfbb4bbc2ccdcf | 0.812324 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.01279 | paper | corpus_metadata | null | Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders | Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that condition... | https://arxiv.org/abs/1704.01279 | 2017-04-05T06:34:22Z | 1704.01279 | 10.48550/arxiv.1704.01279 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.01279 | d659b8b5c6525be5623ba2f138b6151bb610bd33908ef386203872baba740934 | 0.744336 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.02711 | paper | corpus_metadata | null | Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge | This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The perfo... | https://arxiv.org/abs/1708.02711 | 2017-08-09T04:19:42Z | 1708.02711 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1708.02711 | a64b57758e71205a17e893c50aaf140687be4e83130df55fd49dfb70994f2f56 | 0.742876 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.07110 | paper | corpus_metadata | null | Retinal vessel segmentation based on Fully Convolutional Neural Networks | The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a m... | https://arxiv.org/abs/1812.07110 | 2018-12-18T00:14:27Z | 1812.07110 | 10.1016/j.eswa.2018.06.034 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.eswa.2018.06.034 | bc836400a66e7dbc62aebf6ed14518286a475a0c5e9e222f98aa504e7c1d5262 | 0.640863 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.01256 | paper | corpus_metadata | null | Semantic Document Distance Measures and Unsupervised Document Revision Detection | In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detecti... | https://arxiv.org/abs/1709.01256 | 2017-09-05T06:47:03Z | 1709.01256 | 10.48550/arxiv.1709.01256 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1709.01256 | cea37cba37f955ba16b52e94840fceef00f39054105c0cca680d6847e22c4a12 | 0.637761 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.05395 | paper | corpus_metadata | null | Learning Deep Representations of Fine-grained Visual Descriptions | State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories. Despite good perform... | https://arxiv.org/abs/1605.05395 | 2016-05-17T23:08:46Z | 1605.05395 | 10.48550/arxiv.1605.05395 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.05395 | 488c8af53d1d491ac79eea5b75a0aca35869e83c6e9ea849ca04791c89a34e77 | 0.860163 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1507.05717 | paper | corpus_metadata | null | An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition | Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature ext... | https://arxiv.org/abs/1507.05717 | 2015-07-21T06:26:32Z | 1507.05717 | 10.48550/arxiv.1507.05717 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1507.05717 | 1db53be0dd6a918a83230a8c0a9776b7355ef95aed443b6cfb1516f3c2124db5 | 0.819835 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.11910 | paper | corpus_metadata | null | Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference | Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off ... | https://arxiv.org/abs/1810.11910 | 2018-10-29T00:13:50Z | 1810.11910 | 10.48550/arxiv.1810.11910 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1810.11910 | b45b466cb145b3ef671c2cef75c80539919873bcdd9ac5fdbfb0d38e010cf64f | 0.79055 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.09956 | paper | corpus_metadata | null | Pythia v0.1: the Winning Entry to the VQA Challenge 2018 | This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. We demonstrate that by making subtle but important changes to the model architecture and the learn... | https://arxiv.org/abs/1807.09956 | 2018-07-26T04:57:43Z | 1807.09956 | 10.48550/arxiv.1807.09956 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.09956 | 59b37b73708293b27b9bc20c14932d6c0f4c86c5cf0a58421c4c16cd5b9f982b | 0.796464 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.05396 | paper | corpus_metadata | null | Generative Adversarial Text to Image Synthesis | Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convoluti... | https://arxiv.org/abs/1605.05396 | 2016-05-17T23:09:15Z | 1605.05396 | 10.48550/arxiv.1605.05396 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.05396 | 4afbf2dc528f7bc1e7dd4af4f05cb85710e8beeb25433d739c4f3423dad3639e | 0.705921 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.00964 | paper | corpus_metadata | null | Benchmarking machine learning models on multi-centre eICU critical care dataset | Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabl... | https://arxiv.org/abs/1910.00964 | 2019-10-02T14:04:24Z | 1910.00964 | 10.1371/journal.pone.0235424 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1371/journal.pone.0235424 | 5fd65a77dbf9e77a60c54b45011faf408f94e93d29bbf1a102f92d4199e3d74e | 0.776549 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.09393 | paper | corpus_metadata | null | Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation | Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationships in the generated data are much less explored. Natural temporal changes are crucial for sequential generation tasks, e.g. video... | https://arxiv.org/abs/1811.09393 | 2018-11-23T09:16:22Z | 1811.09393 | 10.1145/3386569.3392457 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3386569.3392457 | 51f31781ed1abad83f133dba46334068ec058f5494dad6b26c4fd8782565f824 | 0.845042 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2304.09989 | paper | corpus_metadata | null | CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance | This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms Kmean... | https://arxiv.org/abs/2304.09989 | 2023-04-19T21:46:02Z | 2304.09989 | 10.48550/arxiv.2304.09989 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2304.09989 | 63229da81c3fe425d4b5f0ee63118c2c157c74f26e57a2f4dc95e7943a7d17df | 0.513154 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1610.05775 | paper | corpus_metadata | null | Modeling the Dynamics of Online Learning Activity | People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost ... | https://arxiv.org/abs/1610.05775 | 2016-10-18T20:00:09Z | 1610.05775 | 10.48550/arxiv.1610.05775 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1610.05775 | 96535549ce229d18b2faf2bdadb470cb5f94063465bdf813c93ab91faf70c97c | 0.631232 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.09843 | paper | corpus_metadata | null | Hierarchical Density Order Embeddings | By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" co... | https://arxiv.org/abs/1804.09843 | 2018-04-26T00:43:49Z | 1804.09843 | 10.48550/arxiv.1804.09843 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.09843 | 4a9fe36a0e24c300ee4ce7a924722dccc70d06394aa4642eafd26f2ceb9f8eaf | 0.675285 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.00577 | paper | corpus_metadata | null | Kernalised Multi-resolution Convnet for Visual Tracking | Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on... | https://arxiv.org/abs/1708.00577 | 2017-08-02T02:20:12Z | 1708.00577 | 10.48550/arxiv.1708.00577 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.00577 | 11eb131d6dc3ed4f9ab1c8379cd2b325bad1411404a45fe7e918c0961778db9f | 0.750571 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.02155 | paper | corpus_metadata | null | Spatiotemporal Residual Networks for Video Action Recognition | Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches. Our ... | https://arxiv.org/abs/1611.02155 | 2016-11-07T16:17:16Z | 1611.02155 | 10.48550/arxiv.1611.02155 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.02155 | 4fbca7f0cb8fe74f187064fd5ff34e3984f13191901fd25e03e7ef271ccc29bd | 0.737304 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.07422 | paper | corpus_metadata | null | Generative Partition Networks for Multi-Person Pose Estimation | This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem. Different from existing models that are either completely top-down or bottom-up, the proposed GPN introduces a novel strategy--it generates partitions for multiple persons from their global join... | https://arxiv.org/abs/1705.07422 | 2017-05-21T09:54:48Z | 1705.07422 | 10.48550/arxiv.1705.07422 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1705.07422 | 00fe21fb46b1b0d78c8bcb55f3a10ada77e8959495a41267e74ce08d66ff812c | 0.75889 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.07007 | paper | corpus_metadata | null | Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting | Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph C... | https://arxiv.org/abs/1802.07007 | 2018-02-20T08:40:21Z | 1802.07007 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1802.07007 | 706d02639b5a0930503b570b70bad53ab5563993a01a7403530376f53846b6da | 0.747663 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.01749 | paper | corpus_metadata | null | SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis | This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of th... | https://arxiv.org/abs/1708.01749 | 2017-08-05T10:58:19Z | 1708.01749 | 10.1109/iccv.2017.253 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/iccv.2017.253 | 4b165ca19b3ce91a81c055572cd9e63185e57d49446ee15da173bc2ca7bf338d | 0.697057 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.06211 | paper | corpus_metadata | null | ManifoldNet: A Deep Network Framework for Manifold-valued Data | Deep neural networks have become the main work horse for many tasks involving learning from data in a variety of applications in Science and Engineering. Traditionally, the input to these networks lie in a vector space and the operations employed within the network are well defined on vector-spaces. In the recent past,... | https://arxiv.org/abs/1809.06211 | 2018-09-11T00:27:48Z | 1809.06211 | 10.48550/arxiv.1809.06211 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.06211 | 0ef687d54765dda920d83562ad3ee3bd67ff968f6585c13d40d181b5cc25bdc1 | 0.696153 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1411.4555 | paper | corpus_metadata | null | Show and Tell: A Neural Image Caption Generator | Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation... | https://arxiv.org/abs/1411.4555 | 2014-11-17T17:15:41Z | 1411.4555 | 10.48550/arxiv.1411.4555 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1411.4555 | 6210790e9c1e910034f29bbbea7d819dd402261ee486ecdf0a6f9f79d82070ff | 0.841208 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.10469 | paper | corpus_metadata | null | Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders | Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled represent... | https://arxiv.org/abs/1804.10469 | 2018-04-27T12:37:35Z | 1804.10469 | 10.48550/arxiv.1804.10469 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.10469 | 005cd49e423c760e60f009dd65219097af365e346b85962d8fba987102f9c33b | 0.76343 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
End of preview. Expand in Data Studio
XV-ML full-ledger paper classification
This is the modelomics/xv-ml release, generated from a captured full-ledger pass.
Release modelome-xv-ml-2026-10-08 is complete and ready for release. The manifest is the source of runtime
totals; this card does not invent counts or describe an incomplete manifest as
published. The direct dataset is modelomics/xv-ml.
The capture is bounded by the recorded live-ledger high-water mark. It is a full pass over that current ledger with curated supplements, not a global coverage claim; unresolved source gaps remain possible.
Storage and counts
Parquet files use the canonical train storage split. This is a storage name,
not a training/evaluation benchmark claim.
| Measure | Rows |
|---|---|
classifier_included |
1530803 |
curated_only |
2047 |
examined_rows |
33323692 |
included_rows |
1532850 |
review_rows |
8429993 |
runner_included_rows |
9960930 |
scored_rows |
33313758 |
| Source | Rows |
|---|---|
acl-test-of-time |
13 |
arxiv-complete-snapshot |
700358 |
biorxiv |
2832 |
crossref |
34183 |
crossref-public-data |
56 |
cvpr-longuet-higgins |
5 |
daturkel-landmark-ml-papers |
74 |
dblp-xml-snapshot |
378818 |
epoch-all-ai-models |
1726 |
europe-pmc |
49201 |
gh-ml-hf-daily-papers |
4 |
gh-ml-repository-doi-refs |
14 |
hf-ml-doi-snapshot |
1 |
hf-ml-snapshot |
9 |
icml-test-of-time |
9 |
medrxiv |
390 |
neurips-test-of-time |
3 |
openalex-snapshot-crossfield-ml |
19696 |
openalex-snapshot-cs |
344805 |
openalex-works |
593 |
pubmed-bulk |
5 |
spdin-deep-learning-chronology |
55 |
| Record type | Rows |
|---|---|
blog |
152 |
cited_document |
1168 |
model_card |
248 |
paper |
1531068 |
technical_report |
214 |
| Metadata status | Rows |
|---|---|
| (not reported) | — |
Chronology and coverage are recorded by the run manifest:
| (not reported) | — |
Row schema
Rows preserve title, abstract, DOI or arXiv URL, source, source revision,
source license, article license, group identity, exact text hash, model score,
model inclusion, final inclusion, inclusion reason, classifier or curated
reference provenance, text basis, language, and eligibility status. Each row
also has record_type (paper, technical_report, model_card,
announcement/blog, or cited_document) and metadata_status
(corpus_metadata, corpus_matched, hydrated, citation_only, or
unavailable). Text basis is one of title_abstract, title_only,
abstract_only, or no_text.
This is a broad ML-research dataset, not a claim that every primary record is
a conventional paper. Curated direct-document references remain typed even
when they have no DOI, using a stable source URL key and metadata-availability
flags. A missing document URL remains an audit configuration such as
no_document_reference; it does not create a guessed paper.
An explicit citation without a URL can still retain its reference_label,
title, authors, year, stable source key, and metadata-availability flag.
Curated references are post-hoc additions retained only after an explicit
curator source audit. A curated override is provenance, not evidence that the
classifier has 100% recall. Rows with inclusion_reason=curated_reference
make that override explicit. Curated scholarly references from Epoch and picked
chronologies are retained irrespective of the classifier decision.
The high-recall policy is high_recall_with_review_v1: scorer threshold 0.35, primary/default inclusion threshold 0.5, and every scorer-positive row is retained in the audit collection. Curated references override the score. Non-curated rows in the [0.35, 0.5) band are retained in the optional review_candidates config with included=false, inclusion_reason=review_candidate, and review flags. Language and title-only uncertainty are flags, not vetoes. This review is not a population statistic or an accuracy estimate.
Classifier provenance
| Field | Value |
|---|---|
rubric_version |
ml-methods-neural-apps-v1 |
model_version |
paper-ml-tfidf-logreg-v1 |
model_sha256 |
df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
threshold |
0.35 |
language_policy |
English normlangid confidence >= 0.90 is a review flag after model scoring; it does not veto model-positive inclusion |
provenance.evaluation_counts |
79 |
provenance.language_detector |
langid-1.1.6-normalized |
provenance.training_counts |
320 |
provenance.training_fingerprint |
0d050304377d4727ccf627947c7bc33b76f66710ae6b6852521000864599554c |
| Release metadata | Value |
|---|---|
dataset_scope |
broad_ml_research_v1 |
inclusion_policy |
high_recall_with_review_v1 |
The current classifier was trained with 320 assistant-origin training annotations and evaluated on 79 assistant-origin evaluation annotations under the earlier scope. It remains a coverage anchor for blind spots; it does not define the recall-first inclusion policy. Those metrics are agreement checks and are not validated accuracy, human gold evaluation, or a population estimate. Title-only and language-uncertain rows are retained with flags.
Licensing and reproducibility
Source-specific licenses are preserved per row. This card assigns no global redistribution license, and the release contains paper metadata/abstract fields rather than paper full text. Keep the immutable manifest, captured high-water mark, source revisions, model checksum, chunk receipts, and any curation audit together when reproducing the run.
Files declared by the manifest:
README.mdcoverage/references.parquetcoverage/summary.jsondata/part-00000.parquetdata/part-00001.parquetdata/part-00002.parquetdata/part-00003.parquetdata/part-00004.parquetdata/part-00005.parquetdata/part-00006.parquetdata/part-00007.parquetdata/part-00008.parquetdata/part-00009.parquetdata/part-00010.parquetdata/part-00011.parquetdata/part-00012.parquetdata/part-00013.parquetdata/part-00014.parquetdata/part-00015.parquetdata/part-00016.parquetdata/part-00017.parquetdata/part-00018.parquetdata/part-00019.parquetdata/part-00020.parquetdata/part-00021.parquetdata/part-00022.parquetdata/part-00023.parquetdata/part-00024.parquetdata/part-00025.parquetdata/part-00026.parquetdata/part-00027.parquetdata/part-00028.parquetdata/part-00029.parquetdata/part-00030.parquetdata/part-00031.parquetdata/part-00032.parquetdata/part-00033.parquetdata/part-00034.parquetdata/part-00035.parquetdata/part-00036.parquetdata/part-00037.parquetdata/part-00038.parquetdata/part-00039.parquetdata/part-00040.parquetdata/part-00041.parquetdata/part-00042.parquetdata/part-00043.parquetdata/part-00044.parquetdata/part-00045.parquetdata/part-00046.parquetprovenance/model-manifest.jsonprovenance/source-manifest.jsonreview/data/part-00000.parquetreview/data/part-00001.parquetreview/data/part-00002.parquetreview/data/part-00003.parquetreview/data/part-00004.parquetreview/data/part-00005.parquetreview/data/part-00006.parquetreview/data/part-00007.parquetreview/data/part-00008.parquetreview/data/part-00009.parquetreview/data/part-00010.parquetreview/data/part-00011.parquetreview/data/part-00012.parquetreview/data/part-00013.parquetreview/data/part-00014.parquetreview/data/part-00015.parquetreview/data/part-00016.parquetreview/data/part-00017.parquetreview/data/part-00018.parquetreview/data/part-00019.parquetreview/data/part-00020.parquetreview/data/part-00021.parquetreview/data/part-00022.parquetreview/data/part-00023.parquetreview/data/part-00024.parquetreview/data/part-00025.parquetreview/data/part-00026.parquetreview/data/part-00027.parquetreview/data/part-00028.parquetreview/data/part-00029.parquetreview/data/part-00030.parquetreview/data/part-00031.parquetreview/data/part-00032.parquetreview/data/part-00033.parquetreview/data/part-00034.parquetreview/data/part-00035.parquetreview/data/part-00036.parquetreview/data/part-00037.parquetreview/data/part-00038.parquetreview/data/part-00039.parquetreview/data/part-00040.parquetreview/data/part-00041.parquetreview/data/part-00042.parquetreview/data/part-00043.parquetreview/data/part-00044.parquetreview/data/part-00045.parquetreview/data/part-00046.parquetreview/data/part-00047.parquetreview/data/part-00048.parquetreview/data/part-00049.parquetreview/data/part-00050.parquetreview/data/part-00051.parquetreview/data/part-00052.parquetreview/data/part-00053.parquetreview/data/part-00054.parquetreview/data/part-00055.parquetreview/data/part-00056.parquetreview/data/part-00057.parquetreview/data/part-00058.parquetreview/data/part-00059.parquetreview/data/part-00060.parquetreview/data/part-00061.parquetreview/data/part-00062.parquetreview/data/part-00063.parquetreview/data/part-00064.parquetreview/data/part-00065.parquetreview/data/part-00066.parquetreview/data/part-00067.parquetreview/data/part-00068.parquetreview/data/part-00069.parquetreview/data/part-00070.parquetreview/data/part-00071.parquetreview/data/part-00072.parquetreview/data/part-00073.parquetreview/data/part-00074.parquetreview/data/part-00075.parquetreview/data/part-00076.parquetreview/data/part-00077.parquetreview/data/part-00078.parquetreview/data/part-00079.parquetreview/data/part-00080.parquetreview/data/part-00081.parquetreview/data/part-00082.parquetreview/data/part-00083.parquetreview/data/part-00084.parquetreview/data/part-00085.parquetreview/data/part-00086.parquetreview/data/part-00087.parquetreview/data/part-00088.parquetreview/data/part-00089.parquetreview/data/part-00090.parquetreview/data/part-00091.parquetreview/data/part-00092.parquetreview/data/part-00093.parquetreview/data/part-00094.parquetreview/data/part-00095.parquetreview/data/part-00096.parquetreview/data/part-00097.parquetreview/data/part-00098.parquetreview/data/part-00099.parquetreview/data/part-00100.parquetreview/data/part-00101.parquetreview/data/part-00102.parquetreview/data/part-00103.parquetreview/data/part-00104.parquetreview/data/part-00105.parquetreview/data/part-00106.parquetreview/data/part-00107.parquetreview/data/part-00108.parquetreview/data/part-00109.parquetreview/data/part-00110.parquetreview/data/part-00111.parquetreview/data/part-00112.parquetreview/data/part-00113.parquetreview/data/part-00114.parquetreview/data/part-00115.parquetreview/data/part-00116.parquetreview/data/part-00117.parquetreview/data/part-00118.parquetreview/data/part-00119.parquetreview/data/part-00120.parquetreview/data/part-00121.parquetreview/data/part-00122.parquetreview/data/part-00123.parquetreview/data/part-00124.parquetreview/data/part-00125.parquetreview/data/part-0012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