research
∙
07/20/2023
Clinical Trial Active Learning
This paper presents a novel approach to active learning that takes into ...
share
research
∙
05/24/2023
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
This paper presents a novel positive and negative set selection strategy...
research
∙
04/28/2023
Exploiting the Distortion-Semantic Interaction in Fisheye Data
In this work, we present a methodology to shape a fisheye-specific repre...
research
∙
04/06/2023
Probing the Purview of Neural Networks via Gradient Analysis
We analyze the data-dependent capacity of neural networks and assess ano...
research
∙
02/11/2023
Stochastic Surprisal: An inferential measurement of Free Energy in Neural Networks
This paper conjectures and validates a framework that allows for action ...
research
∙
01/12/2023
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data
This paper considers deep out-of-distribution active learning. In practi...
research
∙
11/10/2022
On the Ramifications of Human Label Uncertainty
Humans exhibit disagreement during data labeling. We term this disagreem...
research
∙
11/09/2022
Clinical Contrastive Learning for Biomarker Detection
This paper presents a novel positive and negative set selection strategy...
research
∙
09/22/2022
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
Clinical diagnosis of the eye is performed over multifarious data modali...
research
∙
09/17/2022
Introspective Learning : A Two-Stage Approach for Inference in Neural Networks
In this paper, we advocate for two stages in a neural network's decision...
research
∙
06/16/2022
Gradient-Based Adversarial and Out-of-Distribution Detection
We propose to utilize gradients for detecting adversarial and out-of-dis...
research
∙
06/16/2022
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation
In seismic interpretation, pixel-level labels of various rock structures...
research
∙
02/24/2022
Explanatory Paradigms in Neural Networks
In this article, we present a leap-forward expansion to the study of exp...
research
∙
03/23/2021
Contrastive Reasoning in Neural Networks
Neural networks represent data as projections on trained weights in a hi...
research
∙
03/23/2021
Extracting Causal Visual Features for Limited label Classification
Neural networks trained to classify images do so by identifying features...
research
∙
08/13/2020
Novelty Detection Through Model-Based Characterization of Neural Networks
In this paper, we propose a model-based characterization of neural netwo...
research
∙
08/04/2020
Implicit Saliency in Deep Neural Networks
In this paper, we show that existing recognition and localization deep a...
research
∙
08/01/2020
Contrastive Explanations in Neural Networks
Visual explanations are logical arguments based on visual features that ...
research
∙
07/18/2020
Backpropagated Gradient Representations for Anomaly Detection
Learning representations that clearly distinguish between normal and abn...
research
∙
08/27/2019
Distorted Representation Space Characterization Through Backpropagated Gradients
In this paper, we utilize weight gradients from backpropagation to chara...
research
∙
02/17/2019
Semantically Interpretable and Controllable Filter Sets
In this paper, we generate and control semantically interpretable filter...
research
∙
11/21/2018
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
In this paper, we train independent linear decoder models to estimate th...
research
∙
11/21/2018
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
In this paper, we introduce an adaptive unsupervised learning framework,...
research
∙
12/07/2017
