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05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
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05/30/2023
Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates
Managing divertor plasmas is crucial for operating reactor scale tokamak...
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05/23/2023
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Neural networks are emerging as a tool for scalable data-driven simulati...
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05/15/2023
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
In this study, we analyze NLG automatic metrics based on whether human e...
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04/04/2023
Equivariant Networks for Porous Crystalline Materials
Efficiently predicting properties of porous crystalline materials has gr...
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11/28/2022
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Recent works have impressively demonstrated that there exists a subnetwo...
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11/17/2022
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations
Neural Stochastic Differential Equations (NSDE) have been trained as bot...
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10/26/2022
Comparison of neural closure models for discretised PDEs
Neural closure models have recently been proposed as a method for effici...
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10/02/2022
Towards Learned Simulators for Cell Migration
Simulators driven by deep learning are gaining popularity as a tool for ...
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08/23/2022
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lottery tickets (LTs) is able to discover accurate and sparse subnetwork...
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05/30/2022
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Recent works on sparse neural network training (sparse training) have sh...
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12/16/2021
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Few-shot classification tasks aim to classify images in query sets based...
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10/01/2021
Calibrated Adversarial Training
Adversarial training is an approach of increasing the robustness of mode...
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09/13/2021
Process Discovery Using Graph Neural Networks
Automatically discovering a process model from an event log is the prime...
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08/27/2021
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
The ability to detect Out-of-Domain (OOD) inputs has been a critical req...
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08/20/2021
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
The goal of a classification model is to assign the correct labels to da...
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07/07/2021
Hierarchical Semantic Segmentation using Psychometric Learning
Assigning meaning to parts of image data is the goal of semantic image s...
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07/06/2021
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model's resilience ag...
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04/19/2021
Direction-Aggregated Attack for Transferable Adversarial Examples
Deep neural networks are vulnerable to adversarial examples that are cra...
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04/16/2021
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Recent years have witnessed an upsurge of interest in the problem of ano...
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11/26/2020
A Metric for Linear Symmetry-Based Disentanglement
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed ...
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11/11/2020
Quantifying and Learning Disentangled Representations with Limited Supervision
Learning low-dimensional representations that disentangle the underlying...
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11/07/2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Deep learning achieves state-of-the-art performance in many tasks but ex...
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09/22/2020
Complex Vehicle Routing with Memory Augmented Neural Networks
Complex real-life routing challenges can be modeled as variations of wel...
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06/14/2020
Explaining Predictions by Approximating the Local Decision Boundary
Constructing accurate model-agnostic explanations for opaque machine lea...
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04/14/2020
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Knowledge present in a domain is well expressed as relationships between...
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01/15/2020
Causal Discovery from Incomplete Data: A Deep Learning Approach
As systems are getting more autonomous with the development of artificia...
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01/14/2020
Pedestrian orientation dynamics from high-fidelity measurements
We investigate in real-life conditions and with very high accuracy the d...
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11/13/2019
Deep learning velocity signals allows to quantify turbulence intensity
Turbulence, the ubiquitous and chaotic state of fluid motions, is charac...
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09/17/2019
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation
This study mainly investigates two decoding problems in neural keyphrase...
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05/23/2019
Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning
Many tasks such as retrieval and recommendations can significantly benef...
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03/26/2019
Micro-expression detection in long videos using optical flow and recurrent neural networks
Facial micro-expressions are subtle and involuntary expressions that can...
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02/07/2019
StampNet: unsupervised multi-class object discovery
Unsupervised object discovery in images involves uncovering recurring pa...
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01/25/2019
Diffusion Variational Autoencoders
A standard Variational Autoencoder, with a Euclidean latent space, is st...
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01/02/2019
Evolutionary Construction of Convolutional Neural Networks
Neuro-Evolution is a field of study that has recently gained significant...
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11/02/2018
Anomaly Detection for imbalanced datasets with Deep Generative Models
Many important data analysis applications present with severely imbalanc...
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09/12/2018
Deep Learning in Information Security
Machine learning has a long tradition of helping to solve complex inform...
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06/09/2017
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Overhead depth map measurements capture sufficient amount of information...
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11/19/2016
Understanding Anatomy Classification Through Visualization
One of the main challenges for broad adoption of deep convolutional neur...
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12/18/2015
