research
∙
06/07/2023
Faithful Knowledge Distillation
Knowledge distillation (KD) has received much attention due to its succe...
share
research
∙
05/23/2023
Expressive Losses for Verified Robustness via Convex Combinations
In order to train networks for verified adversarial robustness, previous...
research
∙
05/17/2023
Provably Correct Physics-Informed Neural Networks
Recent work provides promising evidence that Physics-informed neural net...
research
∙
06/30/2022
Lookback for Learning to Branch
The expressive and computationally inexpensive bipartite Graph Neural Ne...
research
∙
06/29/2022
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
Recent works have tried to increase the verifiability of adversarially t...
research
∙
01/29/2022
A Stochastic Bundle Method for Interpolating Networks
We propose a novel method for training deep neural networks that are cap...
research
∙
01/11/2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Recent multi-task learning research argues against unitary scalarization...
research
∙
01/06/2022
Learning to be adversarially robust and differentially private
We study the difficulties in learning that arise from robust and differe...
research
∙
10/26/2021
Improving Local Effectiveness for Global robust training
Despite its popularity, deep neural networks are easily fooled. To allev...
research
∙
07/27/2021
Neural Network Branch-and-Bound for Neural Network Verification
Many available formal verification methods have been shown to be instanc...
research
∙
07/09/2021
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Randomized smoothing has recently emerged as an effective tool that enab...
research
∙
05/30/2021
Generating Adversarial Examples with Graph Neural Networks
Recent years have witnessed the deployment of adversarial attacks to eva...
research
∙
05/20/2021
Comment on Stochastic Polyak Step-Size: Performance of ALI-G
This is a short note on the performance of the ALI-G algorithm (Berrada ...
research
∙
04/14/2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
We improve the scalability of Branch and Bound (BaB) algorithms for form...
research
∙
02/18/2021
Verifying Probabilistic Specifications with Functional Lagrangians
We propose a general framework for verifying input-output specifications...
research
∙
01/14/2021
Scaling the Convex Barrier with Active Sets
Tight and efficient neural network bounding is of critical importance fo...
research
∙
07/18/2020
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
Recent approaches for weakly supervised instance segmentations depend on...
research
∙
06/26/2020
Hybrid Models for Learning to Branch
A recent Graph Neural Network (GNN) approach for learning to branch has ...
research
∙
02/24/2020
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
research
∙
12/03/2019
Neural Network Branching for Neural Network Verification
Formal verification of neural networks is essential for their deployment...
research
∙
09/14/2019
Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critic...
research
∙
06/13/2019
Training Neural Networks for and by Interpolation
The majority of modern deep learning models are able to interpolate the ...
research
∙
11/25/2018
Dissimilarity Coefficient based Weakly Supervised Object Detection
We consider the problem of weakly supervised object detection, where the...
research
∙
11/19/2018
Deep Frank-Wolfe For Neural Network Optimization
Learning a deep neural network requires solving a challenging optimizati...
research
∙
11/17/2018
Statistical Verification of Neural Networks
We present a new approach to neural network verification based on estima...
research
∙
07/24/2018
Learning Human Poses from Actions
We consider the task of learning to estimate human pose in still images....
research
∙
05/23/2018
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
research
∙
02/21/2018
Smooth Loss Functions for Deep Top-k Classification
The top-k error is a common measure of performance in machine learning a...
research
∙
01/10/2018
Worst-case Optimal Submodular Extensions for Marginal Estimation
Submodular extensions of an energy function can be used to efficiently c...
research
∙
11/26/2017
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify arb...
research
∙
11/01/2017
Piecewise Linear Neural Network verification: A comparative study
The success of Deep Learning and its potential use in many important saf...
research
∙
11/29/2016
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwis...
research
∙
11/07/2016
Trusting SVM for Piecewise Linear CNNs
We present a novel layerwise optimization algorithm for the learning obj...
research
∙
08/22/2016
Efficient Continuous Relaxations for Dense CRF
Dense conditional random fields (CRF) with Gaussian pairwise potentials ...
research
∙
06/08/2016
DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity...
research
∙
12/24/2015
Truncated Max-of-Convex Models
Truncated convex models (TCM) are a special case of pairwise random fiel...
research
∙
07/05/2015
Parsimonious Labeling
We propose a new family of discrete energy minimization problems, which ...
research
∙
08/30/2013
Discriminative Parameter Estimation for Random Walks Segmentation
The Random Walks (RW) algorithm is one of the most e - cient and easy-to...
research
∙
06/05/2013
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
The Random Walks (RW) algorithm is one of the most e - cient and easy-to...
research
∙
06/18/2012
Modeling Latent Variable Uncertainty for Loss-based Learning
We consider the problem of parameter estimation using weakly supervised ...
research
∙
05/09/2012
