Search Results for author:
Found 29 papers, 17 papers with code
Train Ego-Path Detection on Railway Tracks Using End-to-End Deep Learning
This paper introduces the task of "train ego-path detection", a refined approach to railway track detection designed for intelligent onboard vision systems.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs.
Ranked #2 on
Node Property Prediction
on ogbn-arxiv
(using extra training data)
Feature Collapse
We formalize and study a phenomenon called feature collapse that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations.
A Generalization of ViT/MLP-Mixer to Graphs
First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.
Ranked #1 on
Graph Regression
on Peptides-struct
Learning to Untangle Genome Assembly with Graph Convolutional Networks
In this work, we explore a different approach to the central part of the genome assembly task that consists of untangling a large assembly graph from which a genomic sequence needs to be reconstructed.
Long-Tailed Learning Requires Feature Learning
Our data model follows a long-tailed distribution in the sense that some rare subcategories have few representatives in the training set.
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem
This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network.
Graph Neural Networks with Learnable Structural and Positional Representations
An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.
Ranked #12 on
Graph Regression
on ZINC-500k
Genome Sequence Reconstruction Using Gated Graph Convolutional Network
A quest to determine the human DNA sequence from telomere to telomere started three decades ago and was finally finished in 2021.
The Transformer Network for the Traveling Salesman Problem
The Traveling Salesman Problem (TSP) is the most popular and most studied combinatorial problem, starting with von Neumann in 1951.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
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.
Benchmarking Graph Neural Networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
Ranked #1 on
Link Prediction
on COLLAB
On Learning Paradigms for the Travelling Salesman Problem
We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem.
A Two-Step Graph Convolutional Decoder for Molecule Generation
In this work, we introduce a simple two-step decoding process.
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.
GraphTSNE: A Visualization Technique for Graph-Structured Data
Our proposed method GraphTSNE produces visualizations which account for both graph structure and node features.
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
We consider deep linear networks with arbitrary convex differentiable loss.
The Multilinear Structure of ReLU Networks
By appealing to harmonic analysis we show that all local minima of such network are non-differentiable, except for those minima that occur in a region of parameter space where the loss surface is perfectly flat.
Deep linear neural networks with arbitrary loss: All local minima are global
We consider deep linear networks with arbitrary convex differentiable loss.
Residual Gated Graph ConvNets
In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.
Ranked #7 on
Node Classification
on PATTERN 100k
A recurrent neural network without chaos
We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task.
The Product Cut
We introduce a theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective.
Assessing and Improving the Mutation Testing Practice of PIT
We therefore examine how effective are the mutants of a popular mutation testing tool, named PIT, compared to comprehensive ones, as drawn from the literature and personal experience.
Software Engineering
Consistency of Cheeger and Ratio Graph Cuts
We consider point clouds obtained as samples of a ground-truth measure.
An Incremental Reseeding Strategy for Clustering
In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning.
Multiclass Total Variation Clustering
Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation.
Convergence and Energy Landscape for Cheeger Cut Clustering
Unsupervised clustering of scattered, noisy and high-dimensional data points is an important and difficult problem.
