pwc-archive/papers-with-abstracts huggingface dataset:
Convex Optimization-based Policy Adaptation to Compensate for Distributional Shifts
Authors: Navid Hashemi, Justin Ruths, Jyotirmoy V. Deshmukh
Date: 2023-04-05 00:00:00
Tasks: Collision Avoidance, valid

Many real-world systems often involve physical components or operating environments with highly nonlinear and uncertain dynamics. A number of different control algorithms can be used to design optimal controllers for such systems, assuming a reasonably high-fidelity model of the actual system. However, the assumptions made on the stochastic dynamics of the model when designing the optimal controller may no longer be valid when the system is deployed in the real-world. The problem addressed by this paper is the following: Suppose we obtain an optimal trajectory by solving a control problem in the training environment, how do we ensure that the real-world system trajectory tracks this optimal trajectory with minimal amount of error in a deployment environment. In other words, we want to learn how we can adapt an optimal trained policy to distribution shifts in the environment. Distribution shifts are problematic in safety-critical systems, where a trained policy may lead to unsafe outcomes during deployment. We show that this problem can be cast as a nonlinear optimization problem that could be solved using heuristic method such as particle swarm optimization (PSO). However, if we instead consider a convex relaxation of this problem, we can learn policies that track the optimal trajectory with much better error performance, and faster computation times. We demonstrate the efficacy of our approach on tracking an optimal path using a Dubin's car model, and collision avoidance using both a linear and nonlinear model for adaptive cruise control.
Bounding probabilities of causation through the causal marginal problem
Authors: Numair Sani, Atalanti A. Mastakouri, Dominik Janzing
Date: 2023-04-04 00:00:00
Tasks: counterfactual, Decision Making

Probabilities of Causation play a fundamental role in decision making in law, health care and public policy. Nevertheless, their point identification is challenging, requiring strong assumptions such as monotonicity. In the absence of such assumptions, existing work requires multiple observations of datasets that contain the same treatment and outcome variables, in order to establish bounds on these probabilities. However, in many clinical trials and public policy evaluation cases, there exist independent datasets that examine the effect of a different treatment each on the same outcome variable. Here, we outline how to significantly tighten existing bounds on the probabilities of causation, by imposing counterfactual consistency between SCMs constructed from such independent datasets ('causal marginal problem'). Next, we describe a new information theoretic approach on falsification of counterfactual probabilities, using conditional mutual information to quantify counterfactual influence. The latter generalises to arbitrary discrete variables and number of treatments, and renders the causal marginal problem more interpretable. Since the question of 'tight enough' is left to the user, we provide an additional method of inference when the bounds are unsatisfactory: A maximum entropy based method that defines a metric for the space of plausible SCMs and proposes the entropy maximising SCM for inferring counterfactuals in the absence of more information.
Bayesian neural networks via MCMC: a Python-based tutorial
Authors: Rohitash Chandra, Joshua Simmons
Date: 2023-04-02 00:00:00
Tasks: Bayesian Inference, Deep Learning, parameter estimation, Uncertainty Quantification, Variational Inference

Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian inference. In the past three decades, MCMC sampling methods have faced some challenges in being adapted to larger models (such as in deep learning) and big data problems. Advanced proposal distributions that incorporate gradients, such as a Langevin proposal distribution, provide a means to address some of the limitations of MCMC sampling for Bayesian neural networks. Furthermore, MCMC methods have typically been constrained to statisticians and currently not well-known among deep learning researchers. We present a tutorial for MCMC methods that covers simple Bayesian linear and logistic models, and Bayesian neural networks. The aim of this tutorial is to bridge the gap between theory and implementation via coding, given a general sparsity of libraries and tutorials to this end. This tutorial provides code in Python with data and instructions that enable their use and extension. We provide results for some benchmark problems showing the strengths and weaknesses of implementing the respective Bayesian models via MCMC. We highlight the challenges in sampling multi-modal posterior distributions for the case of Bayesian neural networks and the need for further improvement of convergence diagnosis methods.
Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System
Authors: Qihang Zhao, Rui-Jie Zhu, Liu Yang, He Yongming, Bo Zhou, Luo Cheng
Date: 2023-04-05 00:00:00
Tasks: feature selection

In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking. It is generally acknowledged that the model used in the pre-ranking stage must strike a balance between efficacy and efficiency. Thus, the most commonly employed architecture is the representation-focused vector product based model. However, this architecture lacks effective interaction between the query and document, resulting in a reduction in the effectiveness of the search system. To address this issue, we present a novel pre-ranking framework called RankDFM. Our framework leverages DeepFM as the backbone and employs a pairwise training paradigm to learn the ranking of videos under a query. The capability of RankDFM to cross features provides significant improvement in offline and online A/B testing performance. Furthermore, we introduce a learnable feature selection scheme to optimize the model and reduce the time required for online inference, equivalent to a tree model. Currently, RankDFM has been deployed in the search system of a shortvideo App, providing daily services to hundreds of millions users.
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search
Authors: Kim Yong Tan, Yueming Lyu, Yew Soon Ong, Ivor W. Tsang
Date: 2023-04-05 00:00:00
Tasks: Machine Unlearning, Recommendation Systems, Retrieval

Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dependent hashing and achieve good retrieval performance. However, due to increasing demand for users' privacy and security, we often need to remove users' data information from Machine Learning (ML) models to satisfy specific privacy and security requirements. This need requires the ANN search algorithm to support fast online data deletion and insertion. Current learning-based hashing methods need retraining the hash function, which is prohibitable due to the vast time-cost of large-scale data. To address this problem, we propose a novel data-dependent hashing method named unfolded self-reconstruction locality-sensitive hashing (USR-LSH). Our USR-LSH unfolded the optimization update for instance-wise data reconstruction, which is better for preserving data information than data-independent LSH. Moreover, our USR-LSH supports fast online data deletion and insertion without retraining. To the best of our knowledge, we are the first to address the machine unlearning of retrieval problems. Empirically, we demonstrate that USR-LSH outperforms the state-of-the-art data-distribution-independent LSH in ANN tasks in terms of precision and recall. We also show that USR-LSH has significantly faster data deletion and insertion time than learning-based data-dependent hashing.
IndeGx: A Model and a Framework for Indexing RDF Knowledge Graphs with SPARQL-based Test Suits
Authors: Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon, Franck Michel
Date: 2023-01-23 00:00:00
Proceeding: Journal of Web Semantics 2023 1
Tasks: Knowledge Graphs

In recent years, a large number of RDF datasets have been built and published on the Web in fields as diverse as linguistics or life sciences, as well as general datasets such as DBpedia or Wikidata. The joint exploitation of these datasets requires specific knowledge about their content, access points, and commonalities. However, not all datasets contain a self-description, and not all access points can handle the complex queries used to generate such a description. In this article, we provide a standard-based approach to generate the description of a dataset. The generated descriptions as well as the process of their computation are expressed using standard vocabularies and languages. We implemented our approach into a framework, called IndeGx, where each indexing feature and its computation is collaboratively and declaratively defined in a GitHub repository. We have experimented IndeGx on a set of 339 RDF datasets with endpoints listed in public catalogs, over 8 months. The results show that we can collect, as much as possible, important characteristics of the datasets depending on their availability and capacities. The resulting index captures the commonalities, variety, and disparity in the offered content and services and it provides important support to any application designed to query RDF datasets.
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset
Authors: Rahmeh Abou Zafra; Lana Ahmad Abdullah;Rouaa Alaraj; Rasha Albezreh;Tarek Barhoum; Khloud Al Jallad
Date: 2023-04-02 00:00:00
Proceeding: Journal of Current Trends in Computer Science Research(JCTCSR) 2023 4
Tasks: Emotion Recognition, Facial Emotion Recognition

Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of- the-art available datasets still suffer from various problems, such as some unrelated photos such as document photos, unbalanced numbers of photos in each class, and misleading images that can negatively affect correct classification. The 3RL dataset was created, which contains approximately 24K images and will be publicly available, to overcome previously available dataset prob- lems. The 3RL dataset is labelled with five basic emotions: happiness, fear, sadness, disgust, and anger. Moreover, we compared the 3RL dataset with other famous state-of-the-art datasets (FER dataset, CK+ dataset), and we applied the most commonly used algorithms in previous works, SVM and CNN. The results show a noticeable improvement in generalization on the 3RL dataset. Experiments have shown an accuracy of up to 91.4% on 3RL dataset using CNN where results on FER2013, CK+ are, respectively (approximately from 60% to 85%).
Geodesic-Based Bayesian Coherent Point Drift
Authors: Osamu Hirose
Date: 2023-05-01 00:00:00
Proceeding: TPAMI 2023 5

Coherent point drift is a well-known algorithm for non-rigid registration, i.e., a procedure for deforming a shape to match
another shape. Despite its prevalence, the algorithm has a major drawback that remains unsolved: It unnaturally deforms the different
parts of a shape, e.g., human legs, when they are neighboring each other. The inappropriate deformations originate from a proximitybased deformation constraint, called motion coherence. This study proposes a non-rigid registration method that addresses the
drawback. The key to solving the problem is to redefine the motion coherence using a geodesic, i.e., the shortest route between points
on a shape’s surface. We also propose the accelerated variant of the registration method. In numerical studies, we demonstrate that
the algorithms can circumvent the drawback of coherent point drift. We also show that the accelerated algorithm can be applied to
shapes comprising several millions of points.
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
Authors: Alberto Solano, Kevin N. Dietrich, Marcelino Martínez-Sober, Regino Barranquero-Cardeñosa, Jorge Vila-Tomás, Pablo Hernández-Cámara
Date: 2023-03-31 00:00:00
Proceeding: MDPI Applied Sciences 2023 3
Tasks: Deep Learning, Image Segmentation, Retinal Vessel Segmentation, Segmentation, Semantic Segmentation

For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of self-attention-based networks to the field of computer vision through ViTs seems to have changed the trend of using standard convolutions. Throughout this work, we apply different architectures such as U-Net, ViTs and ConvMixer, to compare their performance on a medical semantic segmentation problem. All the models have been trained from scratch on the DRIVE dataset and evaluated on their private counterparts to assess which of the models performed better in the segmentation problem. Our major contribution is showing that the best-performing model (ConvMixer) is the one that shares the approach from the ViT (processing images as patches) while maintaining the foundational blocks (convolutions) from the U-Net. This mixture does not only produce better results (DICE=0.83
) than both ViTs (0.80
/0.077 for UNETR/SWIN-Unet) and the U-Net (0.82
) on their own but reduces considerably the number of parameters (2.97M against 104M/27M and 31M, respectively), showing that there is no need to systematically use large models for solving image problems where smaller architectures with the optimal pieces can get better results.
Optimal operating MR contrast for brain ventricle parcellation
Authors: Savannah P. Hays, Lianrui Zuo, Yuli Wang, Mark G. Luciano, Aaron Carass, Jerry L. Prince
Date: 2023-04-04 00:00:00
Tasks: Anatomy, Image Harmonization

Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy. In this paper, we use image harmonization to explore the impact of different T1-w MR contrasts on a state-of-the-art ventricle parcellation algorithm VParNet. We identify an optimal operating contrast (OOC) for ventricle parcellation; by showing that the performance of a pretrained VParNet can be boosted by adjusting contrast to the OOC.
Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
Authors: Jiayi Guo, Chaofei Wang, You Wu, Eric Zhang, Kai Wang, Xingqian Xu, Shiji Song, Humphrey Shi, Gao Huang
Date: 2023-04-06 00:00:00
Proceeding: CVPR 2023 1
Tasks: Diversity, Image Generation, Prompt Learning

Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-domain samples but only the textual domain labels. The training is highly efficient, e.g., a few minutes. However, existing methods still have some limitations in the quality of generated images and may suffer from the mode collapse issue. A key reason is that a fixed adaptation direction is applied for all cross-domain image pairs, which leads to identical supervision signals. To address this issue, we propose an Image-specific Prompt Learning (IPL) method, which learns specific prompt vectors for each source-domain image. This produces a more precise adaptation direction for every cross-domain image pair, endowing the target-domain generator with greatly enhanced flexibility. Qualitative and quantitative evaluations on various domains demonstrate that IPL effectively improves the quality and diversity of synthesized images and alleviates the mode collapse. Moreover, IPL is independent of the structure of the generative model, such as generative adversarial networks or diffusion models. Code is available at https://github.com/Picsart-AI-Research/IPL-Zero-Shot-Generative-Model-Adaptation.
Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains
Authors: Mingjun Xu, Lingyun Qin, WeiJie Chen, ShiLiang Pu, Lei Zhang
Date: 2023-04-06 00:00:00
Proceeding: CVPR 2023 1
Tasks: Diversity, Domain Generalization, object-detection, Object Detection

Domain shift degrades the performance of object detection models in practical applications. To alleviate the influence of domain shift, plenty of previous work try to decouple and learn the domain-invariant (common) features from source domains via domain adversarial learning (DAL). However, inspired by causal mechanisms, we find that previous methods ignore the implicit insignificant non-causal factors hidden in the common features. This is mainly due to the single-view nature of DAL. In this work, we present an idea to remove non-causal factors from common features by multi-view adversarial training on source domains, because we observe that such insignificant non-causal factors may still be significant in other latent spaces (views) due to the multi-mode structure of data. To summarize, we propose a Multi-view Adversarial Discriminator (MAD) based domain generalization model, consisting of a Spurious Correlations Generator (SCG) that increases the diversity of source domain by random augmentation and a Multi-View Domain Classifier (MVDC) that maps features to multiple latent spaces, such that the non-causal factors are removed and the domain-invariant features are purified. Extensive experiments on six benchmarks show our MAD obtains state-of-the-art performance.
DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model
Authors: Hoigi Seo, Hayeon Kim, Gwanghyun Kim, Se Young Chun
Date: 2023-04-06 00:00:00
Tasks: 3D Object Reconstruction, Diversity, Image to 3D, NeRF, Object, Object Reconstruction, Text to 3D

The increasing demand for high-quality 3D content creation has motivated the development of automated methods for creating 3D object models from a single image and/or from a text prompt. However, the reconstructed 3D objects using state-of-the-art image-to-3D methods still exhibit low correspondence to the given image and low multi-view consistency. Recent state-of-the-art text-to-3D methods are also limited, yielding 3D samples with low diversity per prompt with long synthesis time. To address these challenges, we propose DITTO-NeRF, a novel pipeline to generate a high-quality 3D NeRF model from a text prompt or a single image. Our DITTO-NeRF consists of constructing high-quality partial 3D object for limited in-boundary (IB) angles using the given or text-generated 2D image from the frontal view and then iteratively reconstructing the remaining 3D NeRF using inpainting latent diffusion model. We propose progressive 3D object reconstruction schemes in terms of scales (low to high resolution), angles (IB angles initially to outer-boundary (OB) later), and masks (object to background boundary) in our DITTO-NeRF so that high-quality information on IB can be propagated into OB. Our DITTO-NeRF outperforms state-of-the-art methods in terms of fidelity and diversity qualitatively and quantitatively with much faster training times than prior arts on image/text-to-3D such as DreamFusion, and NeuralLift-360.
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
Authors: Yu Zhang, Xiaoguang Di, Junde Wu, Rao Fu, Yong Li, Yue Wang, Yanwu Xu, Guohui YANG, Chunhui Wang
Date: 2023-04-06 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an optimal high-light image as a reference for low-light image enhancement tasks is impossible, which makes the learning process more difficult than other image processing tasks. As a result, although several low-light image enhancement methods have been proposed, most of them are either too complex or insufficient in addressing all the issues in low-light images. In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions. Specifically, we first recognize the challenges of the need for a large receptive field to obtain global contrast and the lack of an absolute reference, which limits the simplification of network structures in this task. Then, we propose an efficient global feature information extraction component and two loss functions based on relative information to overcome these challenges. Finally, we conducted comparative experiments to demonstrate the effectiveness of the proposed method, and the results confirm that the proposed method can significantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect. The code is available at \url{https://github.com/hitzhangyu/FLW-Net}.
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
Authors: Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Jonathan Ng, HANLIN ZHANG, Scott Emmons, Dan Hendrycks
Date: 2023-04-06 00:00:00
Tasks: Decision Making, Ethics

Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (LMs) may incentivize toxicity. So do agents naturally learn to be Machiavellian? And how do we measure these behaviors in general-purpose models such as GPT-4? Towards answering these questions, we introduce MACHIAVELLI, a benchmark of 134 Choose-Your-Own-Adventure games containing over half a million rich, diverse scenarios that center on social decision-making. Scenario labeling is automated with LMs, which are more performant than human annotators. We mathematize dozens of harmful behaviors and use our annotations to evaluate agents' tendencies to be power-seeking, cause disutility, and commit ethical violations. We observe some tension between maximizing reward and behaving ethically. To improve this trade-off, we investigate LM-based methods to steer agents' towards less harmful behaviors. Our results show that agents can both act competently and morally, so concrete progress can currently be made in machine ethics--designing agents that are Pareto improvements in both safety and capabilities.
Instruction Tuning with GPT-4
Authors: Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao
Date: 2023-04-06 00:00:00
Tasks: Instruction Following

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models. We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training. We make our data generated using GPT-4 as well as our codebase publicly available.