17 citations · 25 across the 12 of their papers we have counts for
13 papers
Chemistry-Inspired Diffusion with Non-Differentiable Guidance
Yuchen Shen, Chenhao Zhang, Sijie Fu +3
Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, throug…
Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
Alan Hsu, Matthew Ho, Joyce Lin +4
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ…
Recovering Time-Varying Networks From Single-Cell Data
Euxhen Hasanaj, Barnabás Póczos, Ziv Bar-Joseph
Gene regulation is a dynamic process that underlies all aspects of human development, disease response, and other key biological processes. The reconstruction of temporal gene regu…
GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
Yuchen Shen, Barnabás Póczos
With the increasing attention to molecular machine learning, various innovations have been made in designing better models or proposing more comprehensive benchmarks. However, less…
Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
Tongzhou Liao, Barnabás Póczos
Graph Neural Networks (GNNs) have become important tools for machine learning on graph-structured data. In this paper, we explore the synergistic combination of graph encoding, gra…
SciFix: Outperforming GPT3 on Scientific Factual Error Correction
Dhananjay Ashok, Atharva Kulkarni, Hai Pham +1
Due to the prohibitively high cost of creating error correction datasets, most Factual Claim Correction methods rely on a powerful verification model to guide the correction proces…
On the Algorithmic Stability and Generalization of Adaptive Optimization Methods
Han Nguyen, Hai Pham, Sashank J. Reddi +1
Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet ful…
Coarse-to-Fine Curriculum Learning
Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell +1
When faced with learning challenging new tasks, humans often follow sequences of steps that allow them to incrementally build up the necessary skills for performing these new tasks…
Re-TACRED: Addressing Shortcomings of the TACRED Dataset
George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos
TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-t…
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
Yiwei Lyu, Paul Pu Liang, Hai Pham +4
Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style…
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos
Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O,…
LucidDream: Controlled Temporally-Consistent DeepDream on Videos
Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos
In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by…
Kernels on Sample Sets via Nonparametric Divergence Estimates
Danica J. Sutherland, Liang Xiong, Barnabás Póczos +1
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algor…