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10/16/2022
3D-GMIC: an efficient deep neural network to find small objects in large 3D images
3D imaging enables a more accurate diagnosis by providing spatial inform...
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02/10/2022
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks
We hypothesize that due to the greedy nature of learning in multi-modal ...
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10/12/2021
Relative Molecule Self-Attention Transformer
Self-supervised learning holds promise to revolutionize molecule propert...
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12/28/2020
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
The early phase of training has been shown to be important in two ways f...
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11/28/2020
Differences between human and machine perception in medical diagnosis
Deep neural networks (DNNs) show promise in image-based medical diagnosi...
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11/25/2020
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
De novo molecule generation often results in chemically unfeasible molec...
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11/19/2020
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks
Collider bias is a harmful form of sample selection bias that neural net...
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08/04/2020
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
During the COVID-19 pandemic, rapid and accurate triage of patients at t...
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06/27/2020
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
One of the key challenges in automated synthesis planning is to generate...
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03/23/2020
Understanding the robustness of deep neural network classifiers for breast cancer screening
Deep neural networks (DNNs) show promise in breast cancer screening, but...
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02/21/2020
The Break-Even Point on Optimization Trajectories of Deep Neural Networks
The early phase of training of deep neural networks is critical for thei...
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02/19/2020
Molecule Attention Transformer
Designing a single neural network architecture that performs competitive...
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06/11/2019
Large Scale Structure of Neural Network Loss Landscapes
There are many surprising and perhaps counter-intuitive properties of op...
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04/06/2019
Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift
Recent work has shown that using unlabeled data in semi-supervised learn...
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03/20/2019
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screeni...
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03/01/2019
Non-linear ICA based on Cramer-Wold metric
Non-linear source separation is a challenging open problem with many app...
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02/02/2019
Parameter-Efficient Transfer Learning for NLP
Fine-tuning large pre-trained models is an effective transfer mechanism ...
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12/27/2018
Neural Architecture Search Over a Graph Search Space
Neural architecture search (NAS) enabled the discovery of state-of-the-a...
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09/24/2018
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function
We demonstrate that in residual neural networks (ResNets) dynamical isom...
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07/13/2018
DNN's Sharpest Directions Along the SGD Trajectory
Recent work has identified that using a high learning rate or a small ba...
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05/23/2018
Cramer-Wold AutoEncoder
We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Follo...
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04/24/2018
Commonsense mining as knowledge base completion? A study on the impact of novelty
Commonsense knowledge bases such as ConceptNet represent knowledge in th...
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11/13/2017
Three Factors Influencing Minima in SGD
We study the properties of the endpoint of stochastic gradient descent (...
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10/13/2017
Residual Connections Encourage Iterative Inference
Residual networks (Resnets) have become a prominent architecture in deep...
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06/16/2017
A Closer Look at Memorization in Deep Networks
We examine the role of memorization in deep learning, drawing connection...
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06/01/2017
Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some wor...
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02/07/2017
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Maybe the single most important goal of representation learning is makin...
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02/19/2016