23 citations · 104 across the 19 of their papers we have counts for
20 papers
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev +2
Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promisin…
FEval-TTC: Fair Evaluation Protocol for Test-Time Compute
Pavel Rumiantsev, Soumyasundar Pal, Yingxue Zhang +1
The performance of Large Language Models (LLMs) and the associated dollar costs of API calls can fluctuate over time, potentially invalidating conclusions drawn in prior research.…
GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
Soumyasundar Pal, Liheng Ma, Amine Natik +2
Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitat…
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…
Extended-BMS Anomalies and Flat Space Holography
Laurent Baulieu, Luca Ciambelli, Tom Wetzstein
We geometrically determine the BRST symmetry of a three-dimensional extended BMS field theory defined at the null boundary of asymptotically flat four-dimensional spaces. We then d…
Refining Answer Distributions for Improved Large Language Model Reasoning
Soumyasundar Pal, Didier Chételat, Yingxue Zhang +1
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Rea…
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Jiaming Zhou, Abbas Ghaddar, Ge Zhang +7
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that in…
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma, Soumyasundar Pal, Yitian Zhang +3
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph do…
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…
Node Copying: A Random Graph Model for Effective Graph Sampling
Florence Regol, Soumyasundar Pal, Jianing Sun +3
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative…
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Soumyasundar Pal, Antonios Valkanas, Florence Regol +1
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learni…
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal, Liheng Ma, Yingxue Zhang +1
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the com…
Node Copying for Protection Against Graph Neural Network Topology Attacks
Florence Regol, Soumyasundar Pal, Mark Coates
Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigati…
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol, Soumyasundar Pal, Yingxue Zhang +1
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can impro…
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Soumyasundar Pal, Saber Malekmohammadi, Florence Regol +3
Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. Ho…
Bayesian Graph Convolutional Neural Networks using Node Copying
Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks. Although the techniques obtain impressive results, they often fall sh…
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, a…
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates +1
Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node an…
Invertible Particle Flow-based Sequential MCMC with extension to Gaussian Mixture noise models
Yunpeng Li, Soumyasundar Pal, Mark Coates
Sequential state estimation in non-linear and non-Gaussian state spaces has a wide range of applications in statistics and signal processing. One of the most effective non-linear f…