📄️ Towards Expressive Graph Neural Networks Beyond Message-Passing
In this project, we develop expressive neural network architectures for learning graphs.
📄️ Diffusing Gaussian Mixtures for Categorical data
Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently emerging diffusion probabilistic models, which have the observed advantage of generating high-quality samples. Recent advances for categorical generative models have focused on log likelihood improvements. In this work, we propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation, and propose sampled-based evaluation methods.
📄️ Multi-label Text Classification in Low Annotation Settings
Abstract
📄️ Early-exit Dynamic Neural Network
In this project we design a universal early-exit framework that can be attached on any backbone network to reduce inference cost by early exiting canonical (simple) samples.
📄️ Multi-resolution Time-Series Forecasting
In this project we propose a novel framework, Multi-resolution Time-Series Transformer (MTST), which consists of a
📄️ A Balanced Neuro-Symbolic Approach for Comonsense Abductive Logic
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising approach for improving LLM reasoning abilities involves translating problems into formal logic and using a logic solver. Although off-the-shelf logic solvers are in principle substantially more efficient than LLMs at logical reasoning, they assume that all relevant facts are provided in a question and are unable to deal with missing commonsense relations. In this work, we propose a novel method that uses feedback from the logic solver to augment a logic problem with commonsense relations provided by the LLM, in an iterative manner. This involves a search procedure through potential commonsense assumptions to maximize the chance of finding useful facts while keeping cost tractable. On a collection of pure-logical reasoning datasets, from which some commonsense information has been removed, our method consistently achieves considerable improvements over existing techniques, demonstrating the value in balancing neural and symbolic elements when working in human contexts.