18 citations · 63 across the 24 of their papers we have counts for
11 papers · 1 filter
Population Monte Carlo with Normalizing Flow
Soumyasundar Pal, Antonios Valkanas, Mark Coates
Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Populat…
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal +2
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patche…
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng, Florence Regol, Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature…
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Florence Regol, Joud Chataoui, Mark Coates
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive perfo…
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Muberra Ozmen, Joseph Cotnareanu, Mark Coates
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains. Most existing approaches require an en…
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma, Chen Lin, Derek Lim +5
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Diffusing Gaussian Mixtures for Generating Categorical Data
Florence Regol, Mark Coates
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 tak…
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang +5
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massiv…
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao +1
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learnin…
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can Chen, Yingxue Zhang, Xue Liu +1
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence d…