pwc-archive/papers-with-abstracts huggingface dataset:
Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion
Authors: Zuxiang Situ, Qi Wang, Shuai Teng, Wanen Feng, Gongfa Chen, Qianqian Zhou, Guangtao Fu
Date: 2023-04-19 00:00:00
Tasks: Bayesian Optimization, Prediction

Deep learning models have become increasingly popular for flood prediction due to their superior accuracy and efficiency compared to traditional methods. However, current machine learning methods often rely on separate spatial or temporal feature analysis and have limitations on the types, number, and dimensions of input data. This study presented a CNN-RNN hybrid feature fusion modelling approach for urban flood prediction, which integrated the strengths of CNNs in processing spatial features and RNNs in analyzing different dimensions of time sequences. This approach allowed for both static and dynamic flood predictions. Bayesian optimization was applied to identify the seven most influential flood-driven factors and determine the best combination strategy. By combining four CNNs (FCN, UNet, SegNet, DeepLabv3+) and three RNNs (LSTM, BiLSTM, GRU), the optimal hybrid model was identified as LSTM-DeepLabv3+. This model achieved the highest prediction accuracy (MAE, RMSE, NSE, and KGE were 0.007, 0.025, 0.973 and 0.755, respectively) under various rainfall input conditions. Additionally, the processing speed was significantly improved, with an inference time of 1.158s (approximately 1/125 of the traditional computation time) compared to the physically-based models.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Authors: Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
Date: 2023-04-19 00:00:00
Tasks: Language Modelling, Large Language Model, Sentiment Analysis

Large language models are becoming increasingly pervasive and ubiquitous in society via deployment in sociotechnical systems. Yet these language models, be it for classification or generation, have been shown to be biased and behave irresponsibly, causing harm to people at scale. It is crucial to audit these language models rigorously. Existing auditing tools leverage either or both humans and AI to find failures. In this work, we draw upon literature in human-AI collaboration and sensemaking, and conduct interviews with research experts in safe and fair AI, to build upon the auditing tool: AdaTest (Ribeiro and Lundberg, 2022), which is powered by a generative large language model (LLM). Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing. To evaluate the effectiveness of the augmented tool, AdaTest++, we conduct user studies with participants auditing two commercial language models: OpenAI's GPT-3 and Azure's sentiment analysis model. Qualitative analysis shows that AdaTest++ effectively leverages human strengths such as schematization, hypothesis formation and testing. Further, with our tool, participants identified a variety of failures modes, covering 26 different topics over 2 tasks, that have been shown before in formal audits and also those previously under-reported.
Beyond Transformers for Function Learning
Authors: Simon Segert, Jonathan Cohen
Date: 2023-04-19 00:00:00
Tasks: Inductive Learning

The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is sufficient to recapitulate the extrapolation abilities of people in this domain. Here, we propose to address this gap by augmenting the transformer architecture with two simple inductive learning biases, that are directly adapted from recent models of abstract reasoning in cognitive science. The results we report demonstrate that these biases are helpful in the context of large neural network models, as well as shed light on the types of inductive learning biases that may contribute to human abilities in extrapolation.
Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision
Authors: Pedro Foletto Pimenta, Pedro H. C. Avelar, Luis C. Lamb
Date: 2023-04-19 00:00:00
Tasks: Graph Neural Network

This paper introduces a new learning-based approach for approximately solving the Kidney-Exchange Problem (KEP), an NP-hard problem on graphs. The problem consists of, given a pool of kidney donors and patients waiting for kidney donations, optimally selecting a set of donations to optimize the quantity and quality of transplants performed while respecting a set of constraints about the arrangement of these donations. The proposed technique consists of two main steps: the first is a Graph Neural Network (GNN) trained without supervision; the second is a deterministic non-learned search heuristic that uses the output of the GNN to find paths and cycles. To allow for comparisons, we also implemented and tested an exact solution method using integer programming, two greedy search heuristics without the machine learning module, and the GNN alone without a heuristic. We analyze and compare the methods and conclude that the learning-based two-stage approach is the best solution quality, outputting approximate solutions on average 1.1 times more valuable than the ones from the deterministic heuristic alone.
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery
Authors: Lalithkumar Seenivasan, Mobarakol Islam, Gokul Kannan, Hongliang Ren
Date: 2023-04-19 00:00:00
Tasks: Question Answering, Scene Segmentation, Visual Question Answering, Visual Question Answering (VQA)

Advances in GPT-based large language models (LLMs) are revolutionizing natural language processing, exponentially increasing its use across various domains. Incorporating uni-directional attention, these autoregressive LLMs can generate long and coherent paragraphs. However, for visual question answering (VQA) tasks that require both vision and language processing, models with bi-directional attention or models employing fusion techniques are often employed to capture the context of multiple modalities all at once. As GPT does not natively process vision tokens, to exploit the advancements in GPT models for VQA in robotic surgery, we design an end-to-end trainable Language-Vision GPT (LV-GPT) model that expands the GPT2 model to include vision input (image). The proposed LV-GPT incorporates a feature extractor (vision tokenizer) and vision token embedding (token type and pose). Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image. Quantitatively, we prove that the LV-GPT model outperforms other state-of-the-art VQA models on two publically available surgical-VQA datasets (based on endoscopic vision challenge robotic scene segmentation 2018 and CholecTriplet2021) and on our newly annotated dataset (based on the holistic surgical scene dataset). We further annotate all three datasets to include question-type annotations to allow sub-type analysis. Furthermore, we extensively study and present the effects of token sequencing, token type and pose embedding for vision tokens in the LV-GPT model.
Learning policies for resource allocation in business processes
Authors: J. Middelhuis, R. Lo Bianco, E. Scherzer, Z. A. Bukhsh, I. J. B. F. Adan, R. M. Dijkman
Date: 2023-04-19 00:00:00
Tasks: Deep Reinforcement Learning, Management

Efficient allocation of resources to activities is pivotal in executing business processes but remains challenging. While resource allocation methodologies are well-established in domains like manufacturing, their application within business process management remains limited. Existing methods often do not scale well to large processes with numerous activities or optimize across multiple cases. This paper aims to address this gap by proposing two learning-based methods for resource allocation in business processes to minimize the average cycle time of cases. The first method leverages Deep Reinforcement Learning (DRL) to learn policies by allocating resources to activities. The second method is a score-based value function approximation approach, which learns the weights of a set of curated features to prioritize resource assignments. We evaluated the proposed approaches on six distinct business processes with archetypal process flows, referred to as scenarios, and three realistically sized business processes, referred to as composite business processes, which are a combination of the scenarios. We benchmarked our methods against traditional heuristics and existing resource allocation methods. The results show that our methods learn adaptive resource allocation policies that outperform or are competitive with the benchmarks in five out of six scenarios. The DRL approach outperforms all benchmarks in all three composite business processes and finds a policy that is, on average, 12.7% better than the best-performing benchmark.
A Latent Space Theory for Emergent Abilities in Large Language Models
Authors: Hui Jiang
Date: 2023-04-19 00:00:00
Tasks: Bayesian Inference, In-Context Learning

Languages are not created randomly but rather to communicate information. There is a strong association between languages and their underlying meanings, resulting in a sparse joint distribution that is heavily peaked according to their correlations. Moreover, these peak values happen to match with the marginal distribution of languages due to the sparsity. With the advent of LLMs trained on big data and large models, we can now precisely assess the marginal distribution of languages, providing a convenient means of exploring the sparse structures in the joint distribution for effective inferences. In this paper, we categorize languages as either unambiguous or {\epsilon}-ambiguous and present quantitative results to demonstrate that the emergent abilities of LLMs, such as language understanding, in-context learning, chain-of-thought prompting, and effective instruction fine-tuning, can all be attributed to Bayesian inference on the sparse joint distribution of languages.
Catch Me If You Can: Identifying Fraudulent Physician Reviews with Large Language Models Using Generative Pre-Trained Transformers
Authors: Aishwarya Deep Shukla, Laksh Agarwal, Jie Mein, Goh, Guodong, Gao, Ritu Agarwal
Date: 2023-04-19 00:00:00

The proliferation of fake reviews of doctors has potentially detrimental consequences for patient well-being and has prompted concern among consumer protection groups and regulatory bodies. Yet despite significant advancements in the fields of machine learning and natural language processing, there remains limited comprehension of the characteristics differentiating fraudulent from authentic reviews. This study utilizes a novel pre-labeled dataset of 38048 physician reviews to establish the effectiveness of large language models in classifying reviews. Specifically, we compare the performance of traditional ML models, such as logistic regression and support vector machines, to generative pre-trained transformer models. Furthermore, we use GPT4, the newest model in the GPT family, to uncover the key dimensions along which fake and genuine physician reviews differ. Our findings reveal significantly superior performance of GPT-3 over traditional ML models in this context. Additionally, our analysis suggests that GPT3 requires a smaller training sample than traditional models, suggesting its appropriateness for tasks with scarce training data. Moreover, the superiority of GPT3 performance increases in the cold start context i.e., when there are no prior reviews of a doctor. Finally, we employ GPT4 to reveal the crucial dimensions that distinguish fake physician reviews. In sharp contrast to previous findings in the literature that were obtained using simulated data, our findings from a real-world dataset show that fake reviews are generally more clinically detailed, more reserved in sentiment, and have better structure and grammar than authentic ones.
Stopping Criteria for Value Iteration on Stochastic Games with Quantitative Objectives
Authors: Jan Křetínský, Tobias Meggendorfer, Maximilian Weininger
Date: 2023-04-19 00:00:00

A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approach is typically preferred over exact techniques, even though no practical bounds on the imprecision of the result could be given until recently. As a consequence, even the most used model checkers could return arbitrarily wrong results. Over the past decade, different works derived stopping criteria, indicating when the precision reaches the desired level, for various settings, in particular MDP with reachability, total reward, and mean payoff, and SG with reachability. In this paper, we provide the first stopping criteria for VI on SG with total reward and mean payoff, yielding the first anytime algorithms in these settings. To this end, we provide the solution in two flavours: First through a reduction to the MDP case and second directly on SG. The former is simpler and automatically utilizes any advances on MDP. The latter allows for more local computations, heading towards better practical efficiency. Our solution unifies the previously mentioned approaches for MDP and SG and their underlying ideas. To achieve this, we isolate objective-specific subroutines as well as identify objective-independent concepts. These structural concepts, while surprisingly simple, form the very essence of the unified solution.
The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
Authors: Vesa Akerman, David Baines, Damien Daspit, Ulf Hermjakob, Taeho Jang, Colin Leong, Michael Martin, Joel Mathew, Jonathan Robie, Marcus Schwarting
Date: 2023-04-19 00:00:00
Tasks: Benchmarking, Machine Translation, NMT, Translation

Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two. Many Christian organizations are dedicated to the task of translating the Holy Bible into languages that lack a modern translation. Bible translation (BT) work is currently underway for over 3000 extremely low resource languages. We introduce the eBible corpus: a dataset containing 1009 translations of portions of the Bible with data in 833 different languages across 75 language families. In addition to a BT benchmarking dataset, we introduce model performance benchmarks built on the No Language Left Behind (NLLB) neural machine translation (NMT) models. Finally, we describe several problems specific to the domain of BT and consider how the established data and model benchmarks might be used for future translation efforts. For a BT task trained with NLLB, Austronesian and Trans-New Guinea language families achieve 35.1 and 31.6 BLEU scores respectively, which spurs future innovations for NMT for low-resource languages in Papua New Guinea.
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Authors: Sanghyun Jo, In-Jae Yu, KyungSu Kim
Date: 2023-04-19 00:00:00
Proceeding: ICCV 2023 1
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels. However, most approaches struggle to produce accurate localization maps and suffer from false predictions in class-related backgrounds (i.e., biased objects), such as detecting a railroad with the train class. Recent methods that remove biased objects require additional supervision for manually identifying biased objects for each problematic class and collecting their datasets by reviewing predictions, limiting their applicability to the real-world dataset with multiple labels and complex relationships for biasing. Following the first observation that biased features can be separated and eliminated by matching biased objects with backgrounds in the same dataset, we propose a fully-automatic/model-agnostic biased removal framework called MARS (Model-Agnostic biased object Removal without additional Supervision), which utilizes semantically consistent features of an unsupervised technique to eliminate biased objects in pseudo labels. Surprisingly, we show that MARS achieves new state-of-the-art results on two popular benchmarks, PASCAL VOC 2012 (val: 77.7%, test: 77.2%) and MS COCO 2014 (val: 49.4%), by consistently improving the performance of various WSSS models by at least 30% without additional supervision.
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation
Authors: Edgardo Solano-Carrillo, Jannis Stoppe
Date: 2023-04-19 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, Imitation Learning, reinforcement-learning, Reinforcement Learning

Domain-adaptive trajectory imitation is a skill that some predators learn for survival, by mapping dynamic information from one domain (their speed and steering direction) to a different domain (current position of the moving prey). An intelligent agent with this skill could be exploited for a diversity of tasks, including the recognition of abnormal motion in traffic once it has learned to imitate representative trajectories. Towards this direction, we propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation using a cycle-consistent generative adversarial method. Our experiments on a variety of synthetic families of reference trajectories show that DATI outperforms baseline methods for imitation learning and optimal control in this setting, keeping the same per-task hyperparameters. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic, opening the door for the use of deep reinforcement learning methods for spatially-unconstrained trajectory data mining.
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Authors: Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho
Date: 2023-04-19 00:00:00
Tasks: Adversarial Robustness

Current studies on adversarial robustness mainly focus on aggregating local robustness results from a set of data samples to evaluate and rank different models. However, the local statistics may not well represent the true global robustness of the underlying unknown data distribution. To address this challenge, this paper makes the first attempt to present a new framework, called GREAT Score , for global robustness evaluation of adversarial perturbation using generative models. Formally, GREAT Score carries the physical meaning of a global statistic capturing a mean certified attack-proof perturbation level over all samples drawn from a generative model. For finite-sample evaluation, we also derive a probabilistic guarantee on the sample complexity and the difference between the sample mean and the true mean. GREAT Score has several advantages: (1) Robustness evaluations using GREAT Score are efficient and scalable to large models, by sparing the need of running adversarial attacks. In particular, we show high correlation and significantly reduced computation cost of GREAT Score when compared to the attack-based model ranking on RobustBench (Croce,et. al. 2021). (2) The use of generative models facilitates the approximation of the unknown data distribution. In our ablation study with different generative adversarial networks (GANs), we observe consistency between global robustness evaluation and the quality of GANs. (3) GREAT Score can be used for remote auditing of privacy-sensitive black-box models, as demonstrated by our robustness evaluation on several online facial recognition services.
ChatGPT as a Therapist Assistant: A Suitability Study
Authors: Mahshid Eshghie, Mojtaba Eshghie
Date: 2023-04-19 00:00:00

This paper proposes using ChatGPT, an innovative technology with various applications, as an assistant for psychotherapy. ChatGPT can serve as a patient information collector, a companion for patients in between therapy sessions, and an organizer of gathered information for therapists to facilitate treatment processes. The research identifies five research questions and discovers useful prompts for fine-tuning the assistant, which shows that ChatGPT can participate in positive conversations, listen attentively, offer validation and potential coping strategies without providing explicit medical advice, and help therapists discover new insights from multiple conversations with the same patient. Using ChatGPT as an assistant for psychotherapy poses several challenges that need to be addressed, including technical as well as human-centric challenges which are discussed.
A Theory on Adam Instability in Large-Scale Machine Learning
Authors: Igor Molybog, Peter Albert, Moya Chen, Zachary DeVito, David Esiobu, Naman Goyal, Punit Singh Koura, Sharan Narang, Andrew Poulton, Ruan Silva, Binh Tang, Diana Liskovich, Puxin Xu, Yuchen Zhang, Melanie Kambadur, Stephen Roller, Susan Zhang
Date: 2023-04-19 00:00:00
Tasks: Language Modeling, Language Modelling

We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant optimization algorithm used for training, called Adam. We observe that Adam can enter a state in which the parameter update vector has a relatively large norm and is essentially uncorrelated with the direction of descent on the training loss landscape, leading to divergence. This artifact is more likely to be observed in the training of a deep model with a large batch size, which is the typical setting of large-scale language model training. To argue the theory, we present observations from the training runs of the language models of different scales: 7 billion, 30 billion, 65 billion, and 546 billion parameters.
Heterogeneous-Agent Reinforcement Learning
Authors: Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang
Date: 2023-04-19 00:00:00
Tasks: LEMMA, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours heavily rely on parameter sharing among agents, which confines them to only homogeneous-agent setting and leads to training instability and lack of convergence guarantees. To achieve effective cooperation in the general heterogeneous-agent setting, we propose Heterogeneous-Agent Reinforcement Learning (HARL) algorithms that resolve the aforementioned issues. Central to our findings are the multi-agent advantage decomposition lemma and the sequential update scheme. Based on these, we develop the provably correct Heterogeneous-Agent Trust Region Learning (HATRL), and derive HATRPO and HAPPO by tractable approximations. Furthermore, we discover a novel framework named Heterogeneous-Agent Mirror Learning (HAML), which strengthens theoretical guarantees for HATRPO and HAPPO and provides a general template for cooperative MARL algorithmic designs. We prove that all algorithms derived from HAML inherently enjoy monotonic improvement of joint return and convergence to Nash Equilibrium. As its natural outcome, HAML validates more novel algorithms in addition to HATRPO and HAPPO, including HAA2C, HADDPG, and HATD3, which generally outperform their existing MA-counterparts. We comprehensively test HARL algorithms on six challenging benchmarks and demonstrate their superior effectiveness and stability for coordinating heterogeneous agents compared to strong baselines such as MAPPO and QMIX.