M. Pawan Kumar

research

∙ 06/07/2023

Faithful Knowledge Distillation

Knowledge distillation (KD) has received much attention due to its succe...

0 Tom A. Lamb, et al. ∙

research

∙ 05/23/2023

Expressive Losses for Verified Robustness via Convex Combinations

In order to train networks for verified adversarial robustness, previous...

0 Alessandro De Palma, et al. ∙

research

∙ 05/17/2023

Provably Correct Physics-Informed Neural Networks

Recent work provides promising evidence that Physics-informed neural net...

0 Francisco Eiras, et al. ∙

research

∙ 06/30/2022

Lookback for Learning to Branch

The expressive and computationally inexpensive bipartite Graph Neural Ne...

5 Prateek Gupta, et al. ∙

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...

0 Alessandro De Palma, et al. ∙

research

∙ 01/29/2022

A Stochastic Bundle Method for Interpolating Networks

We propose a novel method for training deep neural networks that are cap...

0 Alasdair Paren, et al. ∙

research

∙ 01/11/2022

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

Recent multi-task learning research argues against unitary scalarization...

0 Vitaly Kurin, et al. ∙

research

∙ 01/06/2022

Learning to be adversarially robust and differentially private

We study the difficulties in learning that arise from robust and differe...

0 Jamie Hayes, et al. ∙

research

∙ 10/26/2021

Improving Local Effectiveness for Global robust training

Despite its popularity, deep neural networks are easily fooled. To allev...

0 Jingyue Lu, et al. ∙

research

∙ 07/27/2021

Neural Network Branch-and-Bound for Neural Network Verification

Many available formal verification methods have been shown to be instanc...

0 Florian Jaeckle, et al. ∙

research

∙ 07/09/2021

ANCER: Anisotropic Certification via Sample-wise Volume Maximization

Randomized smoothing has recently emerged as an effective tool that enab...

3 Francisco Eiras, et al. ∙

research

∙ 05/30/2021

Generating Adversarial Examples with Graph Neural Networks

Recent years have witnessed the deployment of adversarial attacks to eva...

0 Florian Jaeckle, et al. ∙

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 ...

0 Leonard Berrada, et al. ∙

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...

6 Alessandro De Palma, et al. ∙

research

∙ 02/18/2021

Verifying Probabilistic Specifications with Functional Lagrangians

We propose a general framework for verifying input-output specifications...

0 Leonard Berrada, et al. ∙

research

∙ 01/14/2021

Scaling the Convex Barrier with Active Sets

Tight and efficient neural network bounding is of critical importance fo...

0 Alessandro De Palma, et al. ∙

research

∙ 07/18/2020

Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances

Recent approaches for weakly supervised instance segmentations depend on...

1 Aditya Arun, et al. ∙

research

∙ 06/26/2020

Hybrid Models for Learning to Branch

A recent Graph Neural Network (GNN) approach for learning to branch has ...

0 Prateek Gupta, et al. ∙

research

∙ 02/24/2020

Lagrangian Decomposition for Neural Network Verification

A fundamental component of neural network verification is the computatio...

30 Rudy Bunel, et al. ∙

research

∙ 12/03/2019

Neural Network Branching for Neural Network Verification

Formal verification of neural networks is essential for their deployment...

0 Jingyue Lu, et al. ∙

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...

11 Rudy Bunel, et al. ∙

research

∙ 06/13/2019

Training Neural Networks for and by Interpolation

The majority of modern deep learning models are able to interpolate the ...

0 Leonard Berrada, et al. ∙

research

∙ 11/25/2018

Dissimilarity Coefficient based Weakly Supervised Object Detection

We consider the problem of weakly supervised object detection, where the...

0 Aditya Arun, et al. ∙

research

∙ 11/19/2018

Deep Frank-Wolfe For Neural Network Optimization

Learning a deep neural network requires solving a challenging optimizati...

0 Leonard Berrada, et al. ∙

research

∙ 11/17/2018

Statistical Verification of Neural Networks

We present a new approach to neural network verification based on estima...

0 Stefan Webb, et al. ∙

research

∙ 07/24/2018

Learning Human Poses from Actions

We consider the task of learning to estimate human pose in still images....

2 Aditya Arun, et al. ∙

research

∙ 05/23/2018

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) with Gaussian pairwise potentials...

0 Thomas Joy, et al. ∙

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...

0 Leonard Berrada, et al. ∙

research

∙ 01/10/2018

Worst-case Optimal Submodular Extensions for Marginal Estimation

Submodular extensions of an energy function can be used to efficiently c...

0 Pankaj Pansari, et al. ∙

research

∙ 11/26/2017

Coplanar Repeats by Energy Minimization

This paper proposes an automated method to detect, group and rectify arb...

0 James Pritts, et al. ∙

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...

0 Rudy Bunel, et al. ∙

research

∙ 11/29/2016

Efficient Linear Programming for Dense CRFs

The fully connected conditional random field (CRF) with Gaussian pairwis...

0 Thalaiyasingam Ajanthan, et al. ∙

research

∙ 11/07/2016

Trusting SVM for Piecewise Linear CNNs

We present a novel layerwise optimization algorithm for the learning obj...

0 Leonard Berrada, et al. ∙

research

∙ 08/22/2016

Efficient Continuous Relaxations for Dense CRF

Dense conditional random fields (CRF) with Gaussian pairwise potentials ...

0 Alban Desmaison, et al. ∙

research

∙ 06/08/2016

DISCO Nets: DISsimilarity COefficient Networks

We present a new type of probabilistic model which we call DISsimilarity...

0 Diane Bouchacourt, et al. ∙

research

∙ 12/24/2015

Truncated Max-of-Convex Models

Truncated convex models (TCM) are a special case of pairwise random fiel...

0 Pankaj Pansari, et al. ∙

research

∙ 07/05/2015

Parsimonious Labeling

We propose a new family of discrete energy minimization problems, which ...

0 Puneet K. Dokania, et al. ∙

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...

0 Pierre-Yves Baudin, et al. ∙

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...

0 Pierre-Yves Baudin, et al. ∙

research

∙ 06/18/2012

Modeling Latent Variable Uncertainty for Loss-based Learning

We consider the problem of parameter estimation using weakly supervised ...

0 M. Pawan Kumar, et al. ∙

research

∙ 05/09/2012

MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts

We consider the task of obtaining the maximum a posteriori estimate of d...

0 M. Pawan Kumar, et al. ∙

M. Pawan Kumar

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