pwc-archive/papers-with-abstracts huggingface dataset:
Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models
Authors: Benjamin Laufer, Jon Kleinberg, Hoda Heidari
Date: 2023-08-08 00:00:00

Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) follow a familiar structure: A firm releases a large, pretrained model. It is designed to be adapted and tweaked by other entities to perform particular, domain-specific functions. The model is described as `general-purpose,' meaning it can be transferred to a wide range of downstream tasks, in a process known as adaptation or fine-tuning. Understanding this process - the strategies, incentives, and interactions involved in the development of AI tools - is crucial for making conclusions about societal implications and regulatory responses, and may provide insights beyond AI about general-purpose technologies. We propose a model of this adaptation process. A Generalist brings the technology to a certain level of performance, and one or more Domain specialist(s) adapt it for use in particular domain(s). Players incur costs when they invest in the technology, so they need to reach a bargaining agreement on how to share the resulting revenue before making their investment decisions. We find that for a broad class of cost and revenue functions, there exists a set of Pareto-optimal profit-sharing arrangements where the players jointly contribute to the technology. Our analysis, which utilizes methods based on bargaining solutions and sub-game perfect equilibria, provides insights into the strategic behaviors of firms in these types of interactions. For example, profit-sharing can arise even when one firm faces significantly higher costs than another. After demonstrating findings in the case of one domain-specialist, we provide closed-form and numerical bargaining solutions in the generalized setting with $n$ domain specialists. We find that any potential domain specialization will either contribute, free-ride, or abstain in their uptake of the technology, and provide conditions yielding these different responses.
Event Abstraction for Enterprise Collaboration Systems to Support Social Process Mining
Authors: Jonas Blatt, Patrick Delfmann, Petra Schubert
Date: 2023-08-08 00:00:00

One aim of Process Mining (PM) is the discovery of process models from event logs of information systems. PM has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). ECS event logs are very fine-granular and PM applied to their logs results in spaghetti models. A common solution for this is event abstraction, i.e., converting low-level logs into more abstract high-level logs before running discovery algorithms. ECS logs come with special characteristics that have so far not been fully addressed by existing event abstraction approaches. We aim to close this gap with a tailored ECS event abstraction (ECSEA) approach that trains a model by comparing recorded actual user activities (high-level traces) with the system-generated low-level traces (extracted from the ECS). The model allows us to automatically convert future low-level traces into an abstracted high-level log that can be used for PM. Our evaluation shows that the algorithm produces accurate results. ECSEA is a preprocessing method that is essential for the interpretation of collaborative work activity in ECS, which we call Social Process Mining.
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
Authors: Min Hun Lee, Chong Jun Chew
Date: 2023-08-08 00:00:00
Tasks: counterfactual, Decision Making

Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of achieving human AI complementary performance. In this work, we utilized salient feature explanations along with what-if, counterfactual explanations to make humans review AI suggestions more analytically to reduce overreliance on AI and explored the effect of these explanations on trust and reliance on AI during clinical decision-making. We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality of motion, and analyzed their performance, agreement level on the task, and reliance on AI without and with two types of AI explanations. Our results showed that the AI model with both salient features and counterfactual explanations assisted therapists and laypersons to improve their performance and agreement level on the task when `right' AI outputs are presented. While both therapists and laypersons over-relied on `wrong' AI outputs, counterfactual explanations assisted both therapists and laypersons to reduce their over-reliance on `wrong' AI outputs by 21\% compared to salient feature explanations. Specifically, laypersons had higher performance degrades by 18.0 f1-score with salient feature explanations and 14.0 f1-score with counterfactual explanations than therapists with performance degrades of 8.6 and 2.8 f1-scores respectively. Our work discusses the potential of counterfactual explanations to better estimate the accuracy of an AI model and reduce over-reliance on `wrong' AI outputs and implications for improving human-AI collaborative decision-making.
Some Options for Instantiation of Bipolar Argument Graphs with Deductive Arguments
Authors: Anthony Hunter
Date: 2023-08-08 00:00:00
Tasks: ARC

Argument graphs provide an abstract representation of an argumentative situation. A bipolar argument graph is a directed graph where each node denotes an argument, and each arc denotes the influence of one argument on another. Here we assume that the influence is supporting, attacking, or ambiguous. In a bipolar argument graph, each argument is atomic and so it has no internal structure. Yet to better understand the nature of the individual arguments, and how they interact, it is important to consider their internal structure. To address this need, this paper presents a framework based on the use of logical arguments to instantiate bipolar argument graphs, and a set of possible constraints on instantiating arguments that take into account the internal structure of the arguments, and the types of relationship between arguments.
Cumulative Reasoning with Large Language Models
Authors: Yifan Zhang, Jingqin Yang, Yang Yuan, Andrew Chi-Chih Yao
Date: 2023-08-08 00:00:00
Tasks: Decision Making, Logical Reasoning, Math, Math Word Problem Solving

Despite the recent advancements in language models (LMs), their ability to solve complex problems remains limited. This paper introduces Cumulative Reasoning (CR), a novel approach that utilizes LMs cumulatively and iteratively, mirroring human thought processes for problem-solving. CR decomposes tasks into smaller, manageable components and leverages previous propositions for effective composition, significantly enhancing problem-solving capabilities. We demonstrate CR's superiority through several complex reasoning tasks: it outperforms existing methods in logical inference tasks with up to a 9.3% improvement, achieving 98.04% accuracy on the curated FOLIO wiki dataset. In the Game of 24, it achieves 98% accuracy, marking a 24% improvement over the prior state-of-the-art. Additionally, CR sets new state-of-the-art on the MATH dataset, achieving a 4.2% increase from previous methods and a 43% relative improvement in the most challenging problems. By extending CR to incorporate a code environment without external aids like retrieval or web browsing, we further harness the computational and logical reasoning capabilities of LMs, achieving a remarkable 72.2% accuracy on the MATH dataset and outperforming the PAL/PoT method by 38.8%. Our work not only sets new state-of-the-art but also paves the way toward more sophisticated AI reasoning methods. The code is available at https://github.com/iiis-ai/cumulative-reasoning.
Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographs
Authors: Nickolas Littlefield, Johannes F. Plate, Kurt R. Weiss, Ines Lohse, Avani Chhabra, Ismaeel A. Siddiqui, Zoe Menezes, George Mastorakos, Sakshi Mehul Thakar, Mehrnaz Abedian, Matthew F. Gong, Luke A. Carlson, Hamidreza Moradi, Soheyla Amirian, Ahmad P. Tafti
Date: 2023-08-08 00:00:00
Tasks: Anatomy, Deep Learning, Fairness, Image Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

Automatic segmentation of knee bony anatomy is essential in orthopedics, and it has been around for several years in both pre-operative and post-operative settings. While deep learning algorithms have demonstrated exceptional performance in medical image analysis, the assessment of fairness and potential biases within these models remains limited. This study aims to revisit deep learning-powered knee-bony anatomy segmentation using plain radiographs to uncover visible gender and racial biases. The current contribution offers the potential to advance our understanding of biases, and it provides practical insights for researchers and practitioners in medical imaging. The proposed mitigation strategies mitigate gender and racial biases, ensuring fair and unbiased segmentation results. Furthermore, this work promotes equal access to accurate diagnoses and treatment outcomes for diverse patient populations, fostering equitable and inclusive healthcare provision.
A Lightweight and Accurate Face Detection Algorithm Based on Retinaface
Authors: Baozhu Liu, Hewei Yu
Date: 2023-08-08 00:00:00
Tasks: Face Detection

In this paper, we propose a lightweight and accurate face detection algorithm LAFD (Light and accurate face detection) based on Retinaface. Backbone network in the algorithm is a modified MobileNetV3 network which adjusts the size of the convolution kernel, the channel expansion multiplier of the inverted residuals block and the use of the SE attention mechanism. Deformable convolution network(DCN) is introduced in the context module and the algorithm uses focal loss function instead of cross-entropy loss function as the classification loss function of the model. The test results on the WIDERFACE dataset indicate that the average accuracy of LAFD is 94.1%, 92.2% and 82.1% for the "easy", "medium" and "hard" validation subsets respectively with an improvement of 3.4%, 4.0% and 8.3% compared to Retinaface and 3.1%, 4.1% and 4.1% higher than the well-performing lightweight model, LFFD. If the input image is pre-processed and scaled to 1560px in length or 1200px in width, the model achieves an average accuracy of 86.2% on the 'hard' validation subset. The model is lightweight, with a size of only 10.2MB.
Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra
Authors: Fadhil Muhammad, Alif Bintang Elfandra, Iqbal Pahlevi Amin, Alfan Farizki Wicaksono
Date: 2023-08-08 00:00:00

The abundant biodiversity of coral reefs in Indonesian waters is a valuable asset that needs to be preserved. Rapid climate change and uncontrolled human activities have led to the degradation of coral reef ecosystems, including coral bleaching, which is a critical indicator of coral health conditions. Therefore, this research aims to develop an accurate classification model to distinguish between healthy corals and corals experiencing bleaching. This study utilizes a specialized dataset consisting of 923 images collected from Flickr using the Flickr API. The dataset comprises two distinct classes: healthy corals (438 images) and bleached corals (485 images). These images have been resized to a maximum of 300 pixels in width or height, whichever is larger, to maintain consistent sizes across the dataset. The method employed in this research involves the use of machine learning models, particularly convolutional neural networks (CNN), to recognize and differentiate visual patterns associated with healthy and bleached corals. In this context, the dataset can be used to train and test various classification models to achieve optimal results. By leveraging the ResNet model, it was found that a from-scratch ResNet model can outperform pretrained models in terms of precision and accuracy. The success in developing accurate classification models will greatly benefit researchers and marine biologists in gaining a better understanding of coral reef health. These models can also be employed to monitor changes in the coral reef environment, thereby making a significant contribution to conservation and ecosystem restoration efforts that have far-reaching impacts on life.
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Authors: Lin Yang, Xuchuang Wang, Mohammad Hajiesmaili, Lijun Zhang, John C. S. Lui, Don Towsley
Date: 2023-08-08 00:00:00
Tasks: Multi-Armed Bandits

Recently, there has been extensive study of cooperative multi-agent multi-armed bandits where a set of distributed agents cooperatively play the same multi-armed bandit game. The goal is to develop bandit algorithms with the optimal group and individual regrets and low communication between agents. The prior work tackled this problem using two paradigms: leader-follower and fully distributed algorithms. Prior algorithms in both paradigms achieve the optimal group regret. The leader-follower algorithms achieve constant communication costs but fail to achieve optimal individual regrets. The state-of-the-art fully distributed algorithms achieve optimal individual regrets but fail to achieve constant communication costs. This paper presents a simple yet effective communication policy and integrates it into a learning algorithm for cooperative bandits. Our algorithm achieves the best of both paradigms: optimal individual regret and constant communication costs.
Apple Vision Pro for Healthcare: "The Ultimate Display"? -- Entering the Wonderland of Precision Medicine
Authors: Jan Egger, Christina Gsaxner, Xiaojun Chen, Jiang Bian, Jens Kleesiek, Behrus Puladi
Date: 2023-08-08 00:00:00
Tasks: Mixed Reality

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user's eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the Vision Pro has some interesting features, like an inside-out screen that can show the headset wearers' eyes to "outsiders" or a button on the top, called "Digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the Vision Pro could support clinicians in essential tasks to spend more time with their patients.
Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios
Authors: Amina Ghoul, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi
Date: 2023-08-08 00:00:00
Tasks: Autonomous Driving, Trajectory Prediction

The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps
Authors: Rabbia Asghar, Manuel Diaz-Zapata, Lukas Rummelhard, Anne Spalanzani, Christian Laugier
Date: 2023-08-08 00:00:00
Tasks: Autonomous Vehicles, Motion Forecasting, motion prediction

Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic labels to the occupied cells and incorporating map information. We propose a novel framework that combines deep-learning-based spatio-temporal and probabilistic approaches to predict vehicle behaviors.Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset and show that our model shows superior ability to predict both static and dynamic vehicles compared to OGM predictions. Furthermore, we perform an ablation study and assess the role of semantic labels and map in the architecture.
Actor-Critic with variable time discretization via sustained actions
Authors: Jakub Łyskawa, Paweł Wawrzyński
Date: 2023-08-08 00:00:00
Tasks: Reinforcement Learning (RL)

Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently continuous problems like robotic control, a specific time discretization needs to be defined. This is a choice between sparse time control, which may be easier to train, and finer time control, which may allow for better ultimate performance. In this work, we propose SusACER, an off-policy RL algorithm that combines the advantages of different time discretization settings. Initially, it operates with sparse time discretization and gradually switches to a fine one. We analyze the effects of the changing time discretization in robotic control environments: Ant, HalfCheetah, Hopper, and Walker2D. In all cases our proposed algorithm outperforms state of the art.
Engineering LaCAM$^\ast$: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding
Authors: Keisuke Okumura
Date: 2023-08-08 00:00:00

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.
In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning
Authors: Xiaochuang Han
Date: 2023-08-08 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling

In this note, we explore inference-time alignment through in-context learning. We consider a vanilla pretrained language model Llama-2 before any fine-tuning and retrieve an average of 9 demonstration alignment examples when the model is prompted to follow chat-style instructions. Compared to direct prompting, the in-context alignment without changing model weights leads to a 7x increase in win-rate w.r.t. the text-davinci-003 model from OpenAI, making the vanilla language model comparable to strong baselines with alignment fine-tuning.
Lossy and Lossless (L$^2$) Post-training Model Size Compression
Authors: Yumeng Shi, Shihao Bai, Xiuying Wei, Ruihao Gong, Jianlei Yang
Date: 2023-08-08 00:00:00

Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmission and storage. Many previous studies have explored model size compression. However, these studies often approach various lossy and lossless compression methods in isolation, leading to challenges in achieving high compression ratios efficiently. This work proposes a post-training model size compression method that combines lossy and lossless compression in a unified way. We first propose a unified parametric weight transformation, which ensures different lossy compression methods can be performed jointly in a post-training manner. Then, a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression. Additionally, our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers. Finally, our method can achieve a stable $10\times$ compression ratio without sacrificing accuracy and a $20\times$ compression ratio with minor accuracy loss in a short time. Our code is available at https://github.com/ModelTC/L2_Compression .
Teacher-Student Architecture for Knowledge Distillation: A Survey
Authors: Chengming Hu, Xuan Li, Dan Liu, Haolun Wu, Xi Chen, Ju Wang, Xue Liu
Date: 2023-08-08 00:00:00
Tasks: Knowledge Distillation, regression, Survey

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.