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08/29/2023
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
Neural Machine Translation (NMT) models have been shown to be vulnerable...
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07/07/2023
Online Network Source Optimization with Graph-Kernel MAB
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn...
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07/05/2023
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications
Along with the successful deployment of deep neural networks in several ...
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06/14/2023
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models
In this paper, we propose an optimization-based adversarial attack again...
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06/13/2023
Flexible Channel Dimensions for Differentiable Architecture Search
Finding optimal channel dimensions (i.e., the number of filters in DNN l...
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05/31/2023
Bures-Wasserstein Means of Graphs
Finding the mean of sampled data is a fundamental task in machine learni...
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05/22/2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Task arithmetic has recently emerged as a cost-effective and scalable ap...
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04/21/2023
SequeL: A Continual Learning Library in PyTorch and JAX
Continual Learning is an important and challenging problem in machine le...
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03/04/2023
Hierarchical Training of Deep Neural Networks Using Early Exiting
Deep neural networks provide state-of-the-art accuracy for vision tasks ...
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03/02/2023
Targeted Adversarial Attacks against Neural Machine Translation
Neural Machine Translation (NMT) systems are used in various application...
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02/17/2023
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
This work introduces MiDi, a diffusion model for jointly generating mole...
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02/02/2023
TransFool: An Adversarial Attack against Neural Machine Translation Models
Deep neural networks have been shown to be vulnerable to small perturbat...
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01/07/2023
Adversarial training with informed data selection
With the increasing amount of available data and advances in computing c...
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12/18/2022
Estimating the Adversarial Robustness of Attributions in Text with Transformers
Explanations are crucial parts of deep neural network (DNN) classifiers....
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12/06/2022
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
LiDAR-based 3D object detection is an indispensable task in advanced aut...
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11/01/2022
Maximum Likelihood Distillation for Robust Modulation Classification
Deep Neural Networks are being extensively used in communication systems...
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10/18/2022
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
In Multi-Task Learning, tasks may compete and limit the performance achi...
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10/11/2022
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Fragment-based drug discovery has been an effective paradigm in early-st...
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06/16/2022
Catastrophic overfitting is a bug but also a feature
Despite clear computational advantages in building robust neural network...
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06/07/2022
Fooling Explanations in Text Classifiers
State-of-the-art text classification models are becoming increasingly re...
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05/31/2022
Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
Pooling and unpooling are two essential operations in constructing hiera...
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03/23/2022
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search
Optimizing resource utilization in target platforms is key to achieving ...
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03/22/2022
Scale-out Systolic Arrays
Multi-pod systolic arrays are emerging as the architecture of choice in ...
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03/14/2022
On the benefits of knowledge distillation for adversarial robustness
Knowledge distillation is normally used to compress a big network, or te...
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03/11/2022
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers
Recently, it has been shown that, in spite of the significant performanc...
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03/08/2022
Data augmentation with mixtures of max-entropy transformations for filling-level classification
We address the problem of distribution shifts in test-time data with a p...
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12/27/2021
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
Despite their impressive performance on image classification tasks, deep...
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12/11/2021
Distributed Graph Learning with Smooth Data Priors
Graph learning is often a necessary step in processing or representing s...
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12/03/2021
A Structured Dictionary Perspective on Implicit Neural Representations
Propelled by new designs that permit to circumvent the spectral bias, im...
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10/05/2021
Top-N: Equivariant set and graph generation without exchangeability
We consider one-shot probabilistic decoders that map a vector-shaped pri...
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09/09/2021
FGOT: Graph Distances based on Filters and Optimal Transport
Graph comparison deals with identifying similarities and dissimilarities...
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08/03/2021
Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets
Spectral graph convolutional networks (SGCNs) have been attracting incre...
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07/19/2021
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression
State-of-the-art 2D image compression schemes rely on the power of convo...
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06/28/2021
Privacy-Preserving Image Acquisition Using Trainable Optical Kernel
Preserving privacy is a growing concern in our society where sensors and...
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06/18/2021
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Graph convolution networks, like message passing graph convolution netwo...
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06/12/2021
What can linearized neural networks actually say about generalization?
For certain infinitely-wide neural networks, the neural tangent kernel (...
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05/28/2021
SafeAMC: Adversarial training for robust modulation recognition models
In communication systems, there are many tasks, like modulation recognit...
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05/02/2021
Multi-feature 360 Video Quality Estimation
We propose a new method for the visual quality assessment of 360-degree ...
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04/29/2021
A neural anisotropic view of underspecification in deep learning
The underspecification of most machine learning pipelines means that we ...
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04/27/2021
Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs
Forecasting multivariate time series is challenging as the variables are...
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03/30/2021
Multilayer Graph Clustering with Optimized Node Embedding
We are interested in multilayer graph clustering, which aims at dividing...
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03/27/2021
On the benefits of robust models in modulation recognition
Given the rapid changes in telecommunication systems and their higher de...
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03/16/2021
Bio-inspired Robustness: A Review
Deep convolutional neural networks (DCNNs) have revolutionized computer ...
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03/09/2021
Self-Supervision by Prediction for Object Discovery in Videos
Despite their irresistible success, deep learning algorithms still heavi...
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02/08/2021
Improving filling level classification with adversarial training
We investigate the problem of classifying - from a single image - the le...
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12/24/2020
QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions. This m...
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12/24/2020
Sentence-Based Model Agnostic NLP Interpretability
Today, interpretability of Black-Box Natural Language Processing (NLP) m...
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10/29/2020
FiGLearn: Filter and Graph Learning using Optimal Transport
In many applications, a dataset can be considered as a set of observed s...
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10/29/2020
Multilayer Clustered Graph Learning
Multilayer graphs are appealing mathematical tools for modeling multiple...
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10/19/2020
