Search Results for author: Chaitanya K. Joshi

Found 14 papers, 12 papers with code

Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?

1 code implementation14 Aug 2023 Charles Harris, Kieran Didi, Arian R. Jamasb, Chaitanya K. Joshi, Simon V. Mathis, Pietro Lio, Tom Blundell

Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.


Group Invariant Global Pooling

no code implementations30 May 2023 Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Liò

Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms.

Rotated MNIST

Multi-State RNA Design with Geometric Multi-Graph Neural Networks

1 code implementation24 May 2023 Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò

In this work, we propose gRNAde, a geometric RNA design pipeline that operates on sets of 3D RNA backbone structures to explicitly account for and reflect RNA conformational diversity in its designs.

On the Expressive Power of Geometric Graph Neural Networks

1 code implementation23 Jan 2023 Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò

The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.

On Representation Knowledge Distillation for Graph Neural Networks

1 code implementation9 Nov 2021 Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo

Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings.

Contrastive Learning Knowledge Distillation

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis

no code implementations4 Aug 2021 Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin

In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.

3D Object Classification 3D Part Segmentation +5

Learning the Travelling Salesperson Problem Requires Rethinking Generalization

4 code implementations12 Jun 2020 Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.

Combinatorial Optimization Transfer Learning

Benchmarking Graph Neural Networks

15 code implementations2 Mar 2020 Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Benchmarking Graph Classification +3

Multi-Graph Transformer for Free-Hand Sketch Recognition

1 code implementation24 Dec 2019 Peng Xu, Chaitanya K. Joshi, Xavier Bresson

In this work, we propose a new representation of sketches as multiple sparsely connected graphs.

Sketch Recognition

On Learning Paradigms for the Travelling Salesman Problem

2 code implementations16 Oct 2019 Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem.

reinforcement-learning Reinforcement Learning (RL)

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

3 code implementations4 Jun 2019 Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.

Working women and caste in India: A study of social disadvantage using feature attribution

1 code implementation27 Apr 2019 Kuhu Joshi, Chaitanya K. Joshi

Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work.

BIG-bench Machine Learning

Personalization in Goal-Oriented Dialog

1 code implementation22 Jun 2017 Chaitanya K. Joshi, Fei Mi, Boi Faltings

The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios.

Goal-Oriented Dialog Multi-Task Learning

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