pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Scoring of Students' Science Writing Using Hybrid Neural Network
Authors: Ehsan Latif, Xiaoming Zhai
Date: 2023-12-02 00:00:00
Tasks: regression

This study explores the efficacy of a multi-perspective hybrid neural network (HNN) for scoring student responses in science education with an analytic rubric. We compared the accuracy of the HNN model with four ML approaches (BERT, AACR, Naive Bayes, and Logistic Regression). The results have shown that HHN achieved 8%, 3%, 1%, and 0.12% higher accuracy than Naive Bayes, Logistic Regression, AACR, and BERT, respectively, for five scoring aspects (p<0.001). The overall HNN's perceived accuracy (M = 96.23%, SD = 1.45%) is comparable to the (training and inference) expensive BERT model's accuracy (M = 96.12%, SD = 1.52%). We also have observed that HNN is x2 more efficient in training and inferencing than BERT and has comparable efficiency to the lightweight but less accurate Naive Bayes model. Our study confirmed the accuracy and efficiency of using HNN to score students' science writing automatically.
English to Arabic machine translation of mathematical documents
Authors: Mustapha Eddahibi, Mohammed Mensouri
Date: 2023-12-02 00:00:00
Tasks: Machine Translation, Translation

This paper is about the development of a machine translation system tailored specifically for LATEX mathematical documents. The system focuses on translating English LATEX mathematical documents into Arabic LATEX, catering to the growing demand for multilingual accessibility in scientific and mathematical literature. With the vast proliferation of LATEX mathematical documents the need for an efficient and accurate translation system has become increasingly essential. This paper addresses the necessity for a robust translation tool that enables seamless communication and comprehension of complex mathematical content across language barriers. The proposed system leverages a Transformer model as the core of the translation system, ensuring enhanced accuracy and fluency in the translated Arabic LATEX documents. Furthermore, the integration of RyDArab, an Arabic mathematical TEX extension, along with a rule-based translator for Arabic mathematical expressions, contributes to the precise rendering of complex mathematical symbols and equations in the translated output. The paper discusses the architecture, methodology, of the developed system, highlighting its efficacy in bridging the language gap in the domain of mathematical documentation
Latent SDEs on Homogeneous Spaces
Authors: Sebastian Zeng, Florian Graf, Roland Kwitt
Date: 2023-06-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Inference, Variational Inference

We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE). Motivated by the challenges that arise when trying to learn an (almost arbitrary) latent neural SDE from data, such as efficient gradient computation, we take a step back and study a specific subclass instead. In our case, the SDE evolves on a homogeneous latent space and is induced by stochastic dynamics of the corresponding (matrix) Lie group. In learning problems, SDEs on the unit n-sphere are arguably the most relevant incarnation of this setup. Notably, for variational inference, the sphere not only facilitates using a truly uninformative prior, but we also obtain a particularly simple and intuitive expression for the Kullback-Leibler divergence between the approximate posterior and prior process in the evidence lower bound. Experiments demonstrate that a latent SDE of the proposed type can be learned efficiently by means of an existing one-step geometric Euler-Maruyama scheme. Despite restricting ourselves to a less rich class of SDEs, we achieve competitive or even state-of-the-art results on various time series interpolation/classification problems.
Crystal Structure Prediction by Joint Equivariant Diffusion
Authors: Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, Yang Liu
Date: 2023-07-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Denoising, Prediction

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures -- the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation.
Addressing Negative Transfer in Diffusion Models
Authors: Hyojun Go, Jinyoung Kim, Yunsung Lee, SeungHyun Lee, Shinhyeok Oh, Hyeongdon Moon, Seungtaek Choi
Date: 2023-06-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Clustering, Denoising, Multi-Task Learning

Diffusion-based generative models have achieved remarkable success in various domains. It trains a shared model on denoising tasks that encompass different noise levels simultaneously, representing a form of multi-task learning (MTL). However, analyzing and improving diffusion models from an MTL perspective remains under-explored. In particular, MTL can sometimes lead to the well-known phenomenon of negative transfer, which results in the performance degradation of certain tasks due to conflicts between tasks. In this paper, we first aim to analyze diffusion training from an MTL standpoint, presenting two key observations: (O1) the task affinity between denoising tasks diminishes as the gap between noise levels widens, and (O2) negative transfer can arise even in diffusion training. Building upon these observations, we aim to enhance diffusion training by mitigating negative transfer. To achieve this, we propose leveraging existing MTL methods, but the presence of a huge number of denoising tasks makes this computationally expensive to calculate the necessary per-task loss or gradient. To address this challenge, we propose clustering the denoising tasks into small task clusters and applying MTL methods to them. Specifically, based on (O2), we employ interval clustering to enforce temporal proximity among denoising tasks within clusters. We show that interval clustering can be solved using dynamic programming, utilizing signal-to-noise ratio, timestep, and task affinity for clustering objectives. Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods. We validate the efficacy of proposed clustering and its integration with MTL methods through various experiments, demonstrating 1) improved generation quality and 2) faster training convergence of diffusion models.
Lo-Hi: Practical ML Drug Discovery Benchmark
Authors: Simon Steshin
Date: 2023-10-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Drug Discovery, Molecular Property Prediction, Property Prediction

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties. That is why models for molecular property prediction are being developed and tested on benchmarks such as MoleculeNet. However, existing benchmarks are unrealistic and are too different from applying the models in practice. We have created a new practical \emph{Lo-Hi} benchmark consisting of two tasks: Lead Optimization (Lo) and Hit Identification (Hi), corresponding to the real drug discovery process. For the Hi task, we designed a novel molecular splitting algorithm that solves the Balanced Vertex Minimum $k$-Cut problem. We tested state-of-the-art and classic ML models, revealing which works better under practical settings. We analyzed modern benchmarks and showed that they are unrealistic and overoptimistic. Review: https://openreview.net/forum?id=H2Yb28qGLV Lo-Hi benchmark: https://github.com/SteshinSS/lohi_neurips2023 Lo-Hi splitter library: https://github.com/SteshinSS/lohi_splitter
Mind2Web: Towards a Generalist Agent for the Web
Authors: Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, Yu Su
Date: 2023-06-09 00:00:00
Proceeding: NeurIPS 2023 11

We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents. While the raw HTML of real-world websites are often too large to be fed to LLMs, we show that first filtering it with a small LM significantly improves the effectiveness and efficiency of LLMs. Our solution demonstrates a decent level of performance, even on websites or entire domains the model has never seen before, but there is still a substantial room to improve towards truly generalizable agents. We open-source our dataset, model implementation, and trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further research on building a generalist agent for the web.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Authors: Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
Date: 2023-07-03 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Node Property Prediction, Property Prediction

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.
Deep residual inception encoder-decoder network for amyloid PET harmonization
Authors: Jay Shah, Fei Gao, Baoxin Li, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Yuxiang Zhou, Tammie L.S. Benzinger, Eric M. Reiman, Kewei Chen, Yi Su, Teresa Wu
Date: 2022-02-09 00:00:00
Proceeding: Alzheimer's and Dementia 2022 2
Tasks: Decoder, Image Harmonization, Image-to-Image Translation

Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. A Residual Inception Encoder-Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound-B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10-fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects. Significantly stronger between-tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel-wise measurements in the training cohort and the external testing cohort. We proposed and validated a novel encoder-decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.
What Makes Good Examples for Visual In-Context Learning?
Authors: Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu
Date: 2023-01-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: In-Context Learning, Retrieval

Large-scale models trained on broad data have recently become the mainstream architecture in computer vision due to their strong generalization performance. In this paper, the main focus is on an emergent ability in large vision models, known as in-context learning, which allows inference on unseen tasks by conditioning on in-context examples (a.k.a.~prompt) without updating the model parameters. This concept has been well-known in natural language processing but has only been studied very recently for large vision models. We for the first time provide a comprehensive investigation on the impact of in-context examples in computer vision, and find that the performance is highly sensitive to the choice of in-context examples. To overcome the problem, we propose a prompt retrieval framework to automate the selection of in-context examples. Specifically, we present (1) an unsupervised prompt retrieval method based on nearest example search using an off-the-shelf model, and (2) a supervised prompt retrieval method, which trains a neural network to choose examples that directly maximize in-context learning performance. The results demonstrate that our methods can bring non-trivial improvements to visual in-context learning in comparison to the commonly-used random selection.
GAUCHE: A Library for Gaussian Processes in Chemistry
Authors: Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Samuel Stanton, Gary Tom, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Durholt, Saudamini Chaurasia, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang
Date: 2022-12-06 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Optimisation, Gaussian Processes, Uncertainty Quantification

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Authors: Andy Zhou, Jindong Wang, Yu-Xiong Wang, Haohan Wang
Date: 2023-11-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Data Augmentation, Domain Generalization, Image Classification, Knowledge Distillation

We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation. We address the conjecture that larger models do not make for better teachers by showing strong gains in out-of-distribution robustness when distilling from pretrained foundation models. Following this finding, we propose Discrete Adversarial Distillation (DAD), which leverages a robust teacher to generate adversarial examples and a VQGAN to discretize them, creating more informative samples than standard data augmentation techniques. We provide a theoretical framework for the use of a robust teacher in the knowledge distillation with data augmentation setting and demonstrate strong gains in out-of-distribution robustness and clean accuracy across different student architectures. Notably, our method adds minor computational overhead compared to similar techniques and can be easily combined with other data augmentations for further improvements.
Evaluating The Effectiveness of Capsule Neural Network in Toxic Comment Classification using Pre-trained BERT Embeddings
Authors: Habibur Rahman Sifat, Noor Hossain Nuri Sabab, Tashin Ahmed
Date: 2023-10-12 00:00:00
Proceeding: IEEE Region 10 International Conference TENCON 2023 10
Tasks: Multilingual NLP, Natural Language Understanding, Text Classification, Text Generation, Toxic Comment Classification

Large language models (LLMs) have attracted considerable interest in the fields of natural language understanding (NLU) and natural language generation (NLG) since their introduction. In contrast, the legacy of Capsule Neural Networks (CapsNet) appears to have been largely forgotten amidst all of this excitement. This project's objective is to reignite interest in CapsNet by reopening the previously closed studies and conducting a new research into CapsNet's potential. We present a study where CapsNet is used to classify toxic text by leveraging pre-trained BERT embed dings (bert-base-uncased) on a large multilingual dataset. In this experiment, CapsNet was tasked with categorizing toxic text. By comparing the performance of CapsNet to that of other architectures, such as DistilBERT, Vanilla Neural Networks (VNN), and Convolutional Neural Networks (CNN), we were able to achieve an accuracy of 90.44 %. This result highlights the benefits of CapsNet over text data and suggests new ways to enhance their performance so that it is comparable to DistilBERT and other reduced architectures.
Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme
Authors: Linsheng Guo, Md Habibur Sifat, Tashin Ahmed
Date: 2023-12-01 00:00:00
Proceeding: Proceedings of the First Workshop on Bangla Language Processing (BLP-2023) 2023 12
Tasks: Bangla Text Detection, Classification, Grapheme Detection, Handwritten Text Recognition, Image Categorization, Image Generation, Optical Character Recognition, Optical Character Recognition (OCR), Text Detection, zero-shot-classification, Zero-Shot Learning

This research investigates Zero-Shot Learning (ZSL), and proposes CycleGAN-based image synthesis and accurate label mapping to build a strong association between labels and graphemes. The objective is to enhance model accuracy in detecting unseen classes by employing advanced font image categorization and a CycleGAN-based generator. The resulting representations of abstract character structures demonstrate a significant improvement in recognition, accommodating both seen and unseen classes. This investigation addresses the complex issue of Optical Character Recognition (OCR) in the specific context of the Bangla language. Bangla script is renowned for its intricate nature, consisting of a total of 49 letters, which include 11 vowels, 38 consonants, and 18 diacritics. The combination of letters in this complex arrangement provides the opportunity to create almost 13,000 unique variations of graphemes, which exceeds the number of graphemic units found in the English language. Our investigation presents a new strategy for ZSL in the context of Bangla OCR. This approach combines generative models with careful labeling techniques to enhance the progress of Bangla OCR, specifically focusing on grapheme categorization. Our goal is to make a substantial impact on the digitalization of educational resources in the Indian subcontinent.
Local descriptor-based multi-prototype network for few-shot Learning
Authors: Hongwei Huang 1, Zhangkai Wu 1, Wenbin Li 2, Jing Huo 2, ∗, Yang Gao
Date: 2021-05-05 00:00:00
Proceeding: Pattern Recognition 2021 5
Tasks: Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification

Prototype-based few-shot learning methods are promising in that they are simple yet effective to handle
any-shot problems, and many prototype associated works are raised since then. However, these tradi-
tional prototype-based methods generally use only one single prototype to represent a class, which essen-
tially cannot effectively estimate the complicated distribution of a class. To tackle this problem, we pro-
pose a novel Local descriptor-based Multi-Prototype Network (LMPNet) in this paper, a well-designed frame-
work that generates an embedding space with multiple prototypes. Specifically, the proposed LMPNet
employs local descriptors to represent each image, which can capture more informative and subtler cues
of an image than the normally adopted image-level features. Moreover, to alleviate the uncertainty intro-
duced by the fixed construction (averaging over samples) of prototypes, we introduce a channel squeeze
and spatial excitation (sSE) attention module to learn multiple local descriptor-based prototypes for each
class through end-to-end learning. Extensive experiments on both few-shot and fine-grained few-shot
image classification tasks have been conducted on various benchmark datasets, including mini ImageNet,
tiered ImageNet, Stanford Dogs, Stanford Cars, and CUB-200-2010. The experimental results of our LMPNet
on above datasets show tangibly learning performance improvements and distinguishable outcomes over
the baseline models.