Search Results for author:
Found 20 papers, 15 papers with code
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance.
What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation
To address this, we introduce NOTICE, the first Noise-free Text-Image Corruption and Evaluation pipeline for mechanistic interpretability in VLMs.
Retrieval Augmented Zero-Shot Text Classification
A simple approach often relies on comparing embeddings of query (text) to those of potential classes.
On the Effect of Image Resolution on Semantic Segmentation
We have rigorously tested our method using leading-edge semantic segmentation datasets.
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformations.
One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data
Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving.
Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis
The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis.
Unbalanced CO-Optimal Transport
With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across single-cell measurements.
A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge.
DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
In this paper, we develop a novel attention-based deep learning architecture, DeepDiff, that provides a unified and end-to-end solution to model and to interpret how dependencies among histone modifications control the differential patterns of gene regulation.
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms.
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation.
GaKCo: a Fast GApped k-mer string Kernel using COunting
This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to $O(\Sigma^{M})$.
Memory Matching Networks for Genomic Sequence Classification
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs".
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context.
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification.
DeepChrome: Deep-learning for predicting gene expression from histone modifications
To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model.
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts.
Deep Motif: Visualizing Genomic Sequence Classifications
This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task.
