pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing selectivity using Wasserstein distance based reweighing
Authors: Pratik Worah
Date: 2024-01-21 00:00:00

Given two labeled data-sets $\mathcal{S}$ and $\mathcal{T}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\mathcal{S}$ approaches the limiting distribution that would have resulted by training on $\mathcal{T}$. On the theoretical side, we prove that when the metric entropy of the input datasets is bounded, our greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance. Moreover, the algorithm is simple and scalable, and we prove bounds on the efficiency of the algorithm as well. As a motivating application, we train a neural net to recognize small molecule binders to MNK2 (a MAP Kinase, responsible for cell signaling) which are non-binders to MNK1 (a highly similar protein). In our example dataset, of the 43 distinct small molecules predicted to be most selective from the enamine catalog, 2 small molecules were experimentally verified to be selective, i.e., they reduced the enzyme activity of MNK2 below 50\% but not MNK1, at 10$\mu$M -- a 5\% success rate.
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
Authors: Rodrigo Coelho, André Sequeira, Luís Paulo Santos
Date: 2024-01-21 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networks (NNs), RL is capable of solving extremely complex problems. Deep Q-Learning, a RL algorithm that uses Deep NNs, achieved super-human performance in some specific tasks. Nonetheless, it is also possible to use Variational Quantum Circuits (VQCs) as function approximators in RL algorithms. This work empirically studies the performance and trainability of such VQC-based Deep Q-Learning models in classic control benchmark environments. More specifically, we research how data re-uploading affects both these metrics. We show that the magnitude and the variance of the gradients of these models remain substantial throughout training due to the moving targets of Deep Q-Learning. Moreover, we empirically show that increasing the number of qubits does not lead to an exponential vanishing behavior of the magnitude and variance of the gradients for a PQC approximating a 2-design, unlike what was expected due to the Barren Plateau Phenomenon. This hints at the possibility of VQCs being specially adequate for being used as function approximators in such a context.
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Authors: Siddharth Mansingh, Michal Kucer, Garrett Kenyon, Juston Moore, Michael Teti
Date: 2024-01-21 00:00:00

Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans. Adversarial training, a process where adversarial examples are added to the training set, is the current state-of-the-art defense against adversarial attacks, but it lowers the model's accuracy on clean inputs, is computationally expensive, and offers less robustness to natural noise. In contrast, energy-based models (EBMs), which were designed for efficient implementation in neuromorphic hardware and physical systems, incorporate feedback connections from each layer to the previous layer, yielding a recurrent, deep-attractor architecture which we hypothesize should make them naturally robust. Our work is the first to explore the robustness of EBMs to both natural corruptions and adversarial attacks, which we do using the CIFAR-10 and CIFAR-100 datasets. We demonstrate that EBMs are more robust than transformers and display comparable robustness to adversarially-trained DNNs on gradient-based (white-box) attacks, query-based (black-box) attacks, and natural perturbations without sacrificing clean accuracy, and without the need for adversarial training or additional training techniques.
Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training
Authors: Rongwu Xu, Zhixuan Fang
Date: 2024-01-21 00:00:00
Tasks: CPU, Deep Learning

Cloud deep learning platforms provide cost-effective deep neural network (DNN) training for customers who lack computation resources. However, cloud systems are often untrustworthy and vulnerable to attackers, leading to growing concerns about model privacy. Recently, researchers have sought to protect data privacy in deep learning by leveraging CPU trusted execution environments (TEEs), which minimize the use of cryptography, but existing works failed to simultaneously utilize the computational resources of GPUs to assist in training and prevent model leakage. This paper presents Tempo, the first cloud-based deep learning system that cooperates with TEE and distributed GPUs for efficient DNN training with model confidentiality preserved. To tackle the challenge of preserving privacy while offloading linear algebraic operations from TEE to GPUs for efficient batch computation, we introduce a customized permutation-based obfuscation algorithm to blind both inputs and model parameters. An optimization mechanism that reduces encryption operations is proposed for faster weight updates during backpropagation to speed up training. We implement Tempo and evaluate it with both training and inference for two prevalent DNNs. Empirical results indicate that Tempo outperforms baselines and offers sufficient privacy protection.
CaBuAr: California Burned Areas dataset for delineation
Authors: Daniele Rege Cambrin, Luca Colomba, Paolo Garza
Date: 2024-01-21 00:00:00
Tasks: Burned Area Delineation, Humanitarian

Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools. This paper introduces a novel open dataset that tackles the burned area delineation problem, a binary segmentation problem applied to satellite imagery. The presented resource consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015. Raster annotations were generated from the data released by California's Department of Forestry and Fire Protection. Moreover, in conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models.
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Authors: Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
Date: 2024-01-21 00:00:00

Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria - uniformity, sparsity, or symmetry.
Enhancing Recommendation Diversity by Re-ranking with Large Language Models
Authors: Diego Carraro, Derek Bridge
Date: 2024-01-21 00:00:00
Tasks: Diversity, Recommendation Systems, Re-Ranking

It has long been recognized that it is not enough for a Recommender System (RS) to provide recommendations based only on their relevance to users. Among many other criteria, the set of recommendations may need to be diverse. Diversity is one way of handling recommendation uncertainty and ensuring that recommendations offer users a meaningful choice. The literature reports many ways of measuring diversity and improving the diversity of a set of recommendations, most notably by re-ranking and selecting from a larger set of candidate recommendations. Driven by promising insights from the literature on how to incorporate versatile Large Language Models (LLMs) into the RS pipeline, in this paper we show how LLMs can be used for diversity re-ranking. We begin with an informal study that verifies that LLMs can be used for re-ranking tasks and do have some understanding of the concept of item diversity. Then, we design a more rigorous methodology where LLMs are prompted to generate a diverse ranking from a candidate ranking using various prompt templates with different re-ranking instructions in a zero-shot fashion. We conduct comprehensive experiments testing state-of-the-art LLMs from the GPT and Llama families. We compare their re-ranking capabilities with random re-ranking and various traditional re-ranking methods from the literature. We open-source the code of our experiments for reproducibility. Our findings suggest that the trade-offs (in terms of performance and costs, among others) of LLM-based re-rankers are superior to those of random re-rankers but, as yet, inferior to the ones of traditional re-rankers. However, the LLM approach is promising. LLMs exhibit improved performance on many natural language processing and recommendation tasks and lower inference costs. Given these trends, we can expect LLM-based re-ranking to become more competitive soon.
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
Authors: Wilhelm Kirchgässner, Nikolas Förster, Till Piepenbrock, Oliver Schweins, Oliver Wallscheid
Date: 2024-01-21 00:00:00
Tasks: Feature Engineering

The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores. The following HARDCORE (H-field and power loss estimation for Arbitrary waveforms with Residual, Dilated convolutional neural networks in ferrite COREs) approach shows that a residual convolutional neural network with physics-informed extensions can serve this task efficiently when trained on observational data beforehand. One key solution element is an intermediate model layer which first reconstructs the bh curve and then estimates the power losses based on the curve's area rendering the proposed topology physically interpretable. In addition, emphasis was placed on expert-based feature engineering and information-rich inputs in order to enable a lean model architecture. A model is trained from scratch for each material, while the topology remains the same. A Pareto-style trade-off between model size and estimation accuracy is demonstrated, which yields an optimum at as low as 1755 parameters and down to below 8\,\% for the 95-th percentile of the relative error for the worst-case material with sufficient samples.
Task-specific regularization loss towards model calibration for reliable lung cancer detection
Authors: Mehar Prateek Kalra, Mansi Singhal, Rohan Raju Dhanakashirur
Date: 2024-01-21 00:00:00

Lung cancer is one of the significant causes of cancer-related deaths globally. Early detection and treatment improve the chances of survival. Traditionally CT scans have been used to extract the most significant lung infection information and diagnose cancer. This process is carried out manually by an expert radiologist. The imbalance in the radiologists-to-population ratio in a country like India implies significant work pressure on them and thus raises the need to automate a few of their responsibilities. The tendency of modern-day Deep Neural networks to make overconfident mistakes limit their usage to detect cancer. In this paper, we propose a new task-specific loss function to calibrate the neural network to reduce the risk of overconfident mistakes. We use the state-of-the-art Multi-class Difference in Confidence and Accuracy (MDCA) loss in conjunction with the proposed task-specific loss function to achieve the same. We also integrate post-hoc calibration by performing temperature scaling on top of the train-time calibrated model. We demonstrate 5.98% improvement in the Expected Calibration Error (ECE) and a 17.9% improvement in Maximum Calibration Error (MCE) as compared to the best-performing SOTA algorithm.
Frost Prediction Using Machine Learning Methods in Fars Province
Authors: Milad Barooni, Koorush Ziarati, Ali Barooni
Date: 2024-01-21 00:00:00
Tasks: Deep Learning, Prediction

One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to the country, especially Fars province. Solving this problem requires that, in addition to predicting the minimum temperature, we can provide enough time to implement the necessary measures. Empirical methods have been provided by the Food and Agriculture Organization (FAO), which can predict the minimum temperature, but not in time. In addition to this, we can use machine learning methods to model the minimum temperature. In this study, we have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A customized loss function designed for methods based on deep learning, which can be effective in reducing prediction errors. With methods based on deep learning models, not only do we observe a reduction in RMSE error compared to empirical methods but also have more time to predict minimum temperature. Thus, we can model the minimum temperature for the next 24 hours by having the current 24 hours. With the gradient boosting model (XGBoost) we can keep the prediction time as deep learning and RMSE error reduced. Finally, we experimentally concluded that machine learning methods work better than empirical methods and XGBoost model can have better performance in this problem among other implemented.
Sequential Model for Predicting Patient Adherence in Subcutaneous Immunotherapy for Allergic Rhinitis
Authors: Yin Li, Yu Xiong, Wenxin Fan, Kai Wang, Qingqing Yu, Liping Si, Patrick van der Smagt, Jun Tang, Nutan Chen
Date: 2024-01-21 00:00:00
Tasks: Management, Prediction

Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in three years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM) evaluating them based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60\% to 72\%, and for LSTM models, it is 66\% to 84\%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Authors: Ge Li, Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
Date: 2024-01-21 00:00:00
Tasks: Reinforcement Learning (RL)

Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to implement on real hardware. Episodic RL (ERL) seeks to overcome these challenges by exploring in parameters space that capture the correlation of actions. However, these approaches typically compromise data efficiency, as they treat trajectories as opaque \emph{black boxes}. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. TCE synergistically combines the advantages of step-based and episodic RL, achieving comparable performance to recent ERL methods while maintaining data efficiency akin to state-of-the-art (SoTA) step-based RL.
Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain
Authors: Chen Liang, Yunchen Sheng, Yichen Mo
Date: 2024-01-21 00:00:00
Tasks: Colorization, Image Colorization

Automatic colorization of grayscale image has been a challenging task. Previous research have applied supervised methods in conquering this problem [ 1]. In this paper, we reproduces a GAN-based coloring model, and experiments one of its variant. We also proposed a CycleGAN based model and experiments those methods on various datasets. The result shows that the proposed CycleGAN model does well in human-face coloring and comic coloring, but lack the ability to diverse colorization.
PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs
Authors: David L Cole, Victor M Zavala
Date: 2024-01-21 00:00:00
Tasks: image-classification, Image Classification, Navigate, Time Series, Topological Data Analysis

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages.
MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts
Authors: Haoqiang Guo, Sendong Zhao, Haochun Wang, Yanrui Du, Bing Qin
Date: 2024-01-21 00:00:00
Tasks: Drug Discovery, Language Modeling, Language Modelling, molecular representation, Representation Learning

Deep learning is now widely used in drug discovery, providing significant acceleration and cost reduction. As the most fundamental building block, molecular representation is essential for predicting molecular properties to enable various downstream applications. Most existing methods attempt to incorporate more information to learn better representations. However, not all features are equally important for a specific task. Ignoring this would potentially compromise the training efficiency and predictive accuracy. To address this issue, we propose a novel approach, which treats language models as an agent and molecular pretraining models as a knowledge base. The agent accentuates task-relevant features in the molecular representation by understanding the natural language description of the task, just as a tailor customizes clothes for clients. Thus, we call this approach MolTailor. Evaluations demonstrate MolTailor's superior performance over baselines, validating the efficacy of enhancing relevance for molecular representation learning. This illustrates the potential of language model guided optimization to better exploit and unleash the capabilities of existing powerful molecular representation methods. Our code is available at https://github.com/SCIR-HI/MolTailor.
Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
Authors: Sunil Aryal, Jonathan R. Wells, Arbind Agrahari Baniya, KC Santosh
Date: 2024-01-21 00:00:00
Tasks: Clustering

In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results.
Visual Imitation Learning with Calibrated Contrastive Representation
Authors: Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu
Date: 2024-01-21 00:00:00
Tasks: Contrastive Learning, Decision Making, Imitation Learning

Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions. However, challenges arise in handling visual states due to their less distinguishable representation compared to low-dimensional proprioceptive features. While existing methods resort to adopt complex network architectures or separate the process of learning representation and decision-making, they overlook valuable intra-agent information within demonstrations. To address this problem, this paper proposes a simple and effective solution by incorporating calibrated contrastive representative learning into visual AIL framework. Specifically, we present an image encoder in visual AIL, utilizing a combination of unsupervised and supervised contrastive learning to extract valuable features from visual states. Based on the fact that the improved agent often produces demonstrations of varying quality, we propose to calibrate the contrastive loss by treating each agent demonstrations as a mixed sample. The incorporation of contrastive learning can be jointly optimized with the AIL framework, without modifying the architecture or incurring significant computational costs. Experimental results on DMControl Suite demonstrate our proposed method is sample efficient and can outperform other compared methods from different aspects.