Deep Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 2,693 papers with code (9,423 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Towards Deep Learning Models Resistant to Adversarial Attacks
  19 Jun 2017 59 repositories listed Syntology ran 9 of 17 samples · 8 unverified · 13 pointer-only (licence)
  Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
• Xception: Deep Learning with Depthwise Separable Convolutions
  7 Oct 2016 41 repositories listed Syntology ran 1 of 15 samples · 14 unverified
  We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution…
• Wide & Deep Learning for Recommender Systems
  24 Jun 2016 39 repositories listed Syntology ran 0 of 5 samples · 5 unverified · 5 pointer-only (licence)
  Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
• Relational inductive biases, deep learning, and graph networks
  4 Jun 2018 31 repositories listed Syntology ran 13 of 51 samples · 38 unverified · 15 pointer-only (licence)
  As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
• Deep Learning with Differential Privacy
  1 Jul 2016 26 repositories listed Syntology ran 8 of 24 samples · 16 unverified · 3 pointer-only (licence)
  Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.
• Deep Learning Recommendation Model for Personalization and Recommendation Systems
  31 May 2019 19 repositories listed Syntology ran 4 of 28 samples · 24 unverified · 6 pointer-only (licence)
  With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
• Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
  19 May 2017 19 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 3 pointer-only (licence)
  Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
• A guide to convolution arithmetic for deep learning
  23 Mar 2016 16 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures.
• Deep Interest Evolution Network for Click-Through Rate Prediction
  11 Sep 2018 15 repositories listed Syntology ran 0 of 14 samples · 14 unverified
  Easy-to-use, Modular and Extendible package of deep-learning based CTR models.
• Tensor2Tensor for Neural Machine Translation
  16 Mar 2018 15 repositories listed Syntology ran 1 of 48 samples · 47 unverified
  Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
  16 Dec 2015 14 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout.
• Horovod: fast and easy distributed deep learning in TensorFlow
  15 Feb 2018 13 repositories listed
  Depending on the particular methods employed, this communication may entail anywhere from negligible to significant overhead.
• Revisiting Deep Learning Models for Tabular Data
  22 Jun 2021 11 repositories listed Syntology ran 4 of 22 samples · 18 unverified
  The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets.
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
  15 Mar 2017 11 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 4 pointer-only (licence)
  On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data.
• 3D Bounding Box Estimation Using Deep Learning and Geometry
  1 Dec 2016 11 repositories listed Syntology ran 3 of 27 samples · 24 unverified · 5 pointer-only (licence)
  In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these…
• Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
  15 Oct 2016 11 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN).
• The Limitations of Deep Learning in Adversarial Settings
  24 Nov 2015 11 repositories listed
  In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between…
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
  18 Jun 2020 10 repositories listed Syntology ran 4 of 19 samples · 15 unverified
  Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle…
• InceptionTime: Finding AlexNet for Time Series Classification
  11 Sep 2019 10 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
• Deep Learning for Classical Japanese Literature
  3 Dec 2018 10 repositories listed Syntology ran 2 of 11 samples · 9 unverified · 1 pointer-only (licence)
  Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks.
• Evidential Deep Learning to Quantify Classification Uncertainty
  5 Jun 2018 10 repositories listed Syntology ran 4 of 5 samples · 1 unverified
  Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems.
• NiftyNet: a deep-learning platform for medical imaging
  11 Sep 2017 10 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
• Deep learning with Elastic Averaging SGD
  20 Dec 2014 10 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance.
• Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
  13 Aug 2022 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point.
• Learning to Reweight Examples for Robust Deep Learning
  24 Mar 2018 9 repositories listed
  Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.
• A Simple Baseline for Bayesian Uncertainty in Deep Learning
  7 Feb 2019 8 repositories listed Syntology ran 6 of 17 samples · 11 unverified
  We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.
• TensorFlow Distributions
  28 Nov 2017 8 repositories listed
  The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
• DGM: A deep learning algorithm for solving partial differential equations
  24 Aug 2017 8 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  The algorithm is tested on a class of high-dimensional free boundary PDEs, which we are able to accurately solve in up to $200$ dimensions.
• Deep Learning based Recommender System: A Survey and New Perspectives
  24 Jul 2017 8 repositories listed
  This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.
• SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
  2 Jun 2021 7 repositories listed Syntology ran 3 of 26 samples · 23 unverified · 13 pointer-only (licence)
  We devise a hybrid deep learning approach to solving tabular data problems.

Syntology lines on 25 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.