Search Results for author:
Found 15 papers, 11 papers with code
Scalable Neural Network Kernels
We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP).
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP.
Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning
Our work opens the scope of using our method to radiologist's reports on various body parts.
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models
Transformers are state-of-the-art deep learning models that are composed of stacked attention and point-wise, fully connected layers designed for handling sequential data.
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.
Learning Full Configuration Interaction Electron Correlations with Deep Learning
In this report, we present a deep learning framework termed the Electron Correlation Potential Neural Network (eCPNN) that can learn succinct and compact potential functions.
Permutation invariant networks to learn Wasserstein metrics
In this work, we use a permutation invariant network to map samples from probability measures into a low-dimensional space such that the Euclidean distance between the encoded samples reflects the Wasserstein distance between probability measures.
Self-supervised edge features for improved Graph Neural Network training
In recent years, there has been a lot of work incorporating edge features along with node features for prediction tasks.
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic.
Explainable Artificial Intelligence (XAI)
General Classification
+3
Learning Potentials of Quantum Systems using Deep Neural Networks
Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature.
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data.
Using Chinese Glyphs for Named Entity Recognition
In this paper for Chinese NER systems, we do not use these traditional features but we use lexicographic features of Chinese characters.
