pwc-archive/papers-with-abstracts huggingface dataset:
Gaussian Process Regression under Computational and Epistemic Misspecification
Authors: Daniel Sanz-Alonso, Ruiyi Yang
Date: 2023-12-14 00:00:00
Tasks: regression

Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the interpolation error. We introduce a unified framework to analyze Gaussian process regression under important classes of computational misspecification: Karhunen-Lo\`eve expansions that result in low-rank kernel approximations, multiscale wavelet expansions that induce sparsity in the covariance matrix, and finite element representations that induce sparsity in the precision matrix. Our theory also accounts for epistemic misspecification in the choice of kernel parameters.
Featurizing Koopman Mode Decomposition For Robust Forecasting
Authors: David Aristoff, Jeremy Copperman, Nathan Mankovich, Alexander Davies
Date: 2023-12-14 00:00:00
Tasks: Dimensionality Reduction

This article introduces an advanced Koopman mode decomposition (KMD) technique -- coined Featurized Koopman Mode Decomposition (FKMD) -- that uses delay embedding and a learned Mahalanobis distance to enhance analysis and prediction of high dimensional dynamical systems. The delay embedding expands the observation space to better capture underlying manifold structure, while the Mahalanobis distance adjusts observations based on the system's dynamics. This aids in featurizing KMD in cases where good features are not a priori known. We show that FKMD improves predictions for a high-dimensional linear oscillator, a high-dimensional Lorenz attractor that is partially observed, and a cell signaling problem from cancer research.
Using Surprise Index for Competency Assessment in Autonomous Decision-Making
Authors: Akash Ratheesh, Ofer Dagan, Nisar R. Ahmed, Jay McMahon
Date: 2023-12-14 00:00:00
Tasks: Decision Making

This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments. The inherent opacity of machine learning models, from the perspective of the user, often described as a `black box', poses a challenge. To overcome this, we propose using a measure called the Surprise index, which leverages available measurement data to quantify whether the dynamic system performs as expected. We show that the surprise index can be computed in closed form for dynamic systems when observed evidence in a probabilistic model if the joint distribution for that evidence follows a multivariate Gaussian marginal distribution. We then apply it to a nonlinear spacecraft maneuver problem, where actions are chosen by a reinforcement learning agent and show it can indicate how well the trajectory follows the required orbit.
Space-Time Approximation with Shallow Neural Networks in Fourier Lebesgue spaces
Authors: Ahmed Abdeljawad, Thomas Dittrich
Date: 2023-12-13 00:00:00

Approximation capabilities of shallow neural networks (SNNs) form an integral part in understanding the properties of deep neural networks (DNNs). In the study of these approximation capabilities some very popular classes of target functions are the so-called spectral Barron spaces. This spaces are of special interest when it comes to the approximation of partial differential equation (PDE) solutions. It has been shown that the solution of certain static PDEs will lie in some spectral Barron space. In order to alleviate the limitation to static PDEs and include a time-domain that might have a different regularity than the space domain, we extend the notion of spectral Barron spaces to anisotropic weighted Fourier-Lebesgue spaces. In doing so, we consider target functions that have two blocks of variables, among which each block is allowed to have different decay and integrability properties. For these target functions we first study the inclusion of anisotropic weighted Fourier-Lebesgue spaces in the Bochner-Sobolev spaces. With that we can now also measure the approximation error in terms of an anisotropic Sobolev norm, namely the Bochner-Sobolev norm. We use this observation in a second step where we establish a bound on the approximation rate for functions from the anisotropic weighted Fourier-Lebesgue spaces and approximation via SNNs in the Bochner-Sobolev norm.
Universal approximation property of Banach space-valued random feature models including random neural networks
Authors: Ariel Neufeld, Philipp Schmocker
Date: 2023-12-13 00:00:00

We introduce a Banach space-valued extension of random feature learning, a data-driven supervised machine learning technique for large-scale kernel approximation. By randomly initializing the feature maps, only the linear readout needs to be trained, which reduces the computational complexity substantially. Viewing random feature models as Banach space-valued random variables, we prove a universal approximation result in the corresponding Bochner space. Moreover, we derive approximation rates and an explicit algorithm to learn an element of the given Banach space by such models. The framework of this paper includes random trigonometric/Fourier regression and in particular random neural networks which are single-hidden-layer feedforward neural networks whose weights and biases are randomly initialized, whence only the linear readout needs to be trained. For the latter, we can then lift the universal approximation property of deterministic neural networks to random neural networks, even within function spaces over non-compact domains, e.g., weighted spaces, $L^p$-spaces, and (weighted) Sobolev spaces, where the latter includes the approximation of the (weak) derivatives. In addition, we analyze when the training costs for approximating a given function grow polynomially in both the input/output dimension and the reciprocal of a pre-specified tolerated approximation error. Furthermore, we demonstrate in a numerical example the empirical advantages of random feature models over their deterministic counterparts.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Authors: Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng, Yunfeng Zhao, Guoxian Yu, Shuguang Han, Yuning Jiang, Jian Xu, Bo Zheng
Date: 2023-12-14 00:00:00
Tasks: Click-Through Rate Prediction

In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recognized the importance of privileged features and explored ways to tackle online-offline discrepancies. A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving. In practice, the pointwise cross-entropy loss is often adopted for PFD. However, this loss is insufficient to distill the ranking ability for CTR prediction. First, it does not consider the non-i.i.d. characteristic of the data distribution, i.e., other items on the same page significantly impact the click probability of the candidate item. Second, it fails to consider the relative item order ranked by the teacher model's predictions, which is essential to distill the ranking ability. To address these issues, we first extend the pointwise-based PFD to the listwise-based PFD. We then define the calibration-compatible property of distillation loss and show that commonly used listwise losses do not satisfy this property when employed as distillation loss, thus compromising the model's calibration ability, which is another important measure for CTR prediction. To tackle this dilemma, we propose Calibration-compatible LIstwise Distillation (CLID), which employs carefully-designed listwise distillation loss to achieve better ranking ability than the pointwise-based PFD while preserving the model's calibration ability. We theoretically prove it is calibration-compatible. Extensive experiments on public datasets and a production dataset collected from the display advertising system of Alibaba further demonstrate the effectiveness of CLID.
Hybrid Content Dynamic Recommendation System Based in Adapted Tags and Applied to Digital Library
Authors: Thiago Bellotti Furtado, Ahmed Esmin
Date: 2023-12-14 00:00:00

The technological evolution of the library in the academic environment brought a lot of information and documents that are available to access, but these systems do not always have mechanisms to search in an integrated way the relevant information for the user. To alleviate this problem, we propose a recommendation system that generates the user profile through tags that are reshaped over time. To trace the user profile the system uses information from your lending history stored in the library database and it collects their opinions (feedback) through a list of recommendations. These data are integrated with the document base of institutional repository.Thus, the recommendation system assists users in identifying relevant items and makes suggestions for content in an integrated environment that contains institutional repository documents and the university library database. The proposed recommendation system uses a hybrid approach being applied in an academic environment with the participation of the users.
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
Authors: Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
Date: 2023-10-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Continual Learning, Incremental Learning

For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The incremental learning of a growing sequence of tasks holds promise to enable accurate classification even with few samples per task by leveraging information from all the tasks in the sequence (forward and backward learning). However, existing techniques developed for continual learning and concept drift adaptation are either designed for tasks with time-independent similarities or only aim to learn the last task in the sequence. This paper presents incremental minimax risk classifiers (IMRCs) that effectively exploit forward and backward learning and account for evolving tasks. In addition, we analytically characterize the performance improvement provided by forward and backward learning in terms of the tasks' expected quadratic change and the number of tasks. The experimental evaluation shows that IMRCs can result in a significant performance improvement, especially for reduced sample sizes.
Recasting Continual Learning as Sequence Modeling
Authors: Soochan Lee, Jaehyeon Son, Gunhee Kim
Date: 2023-10-18 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Continual Learning

In this work, we aim to establish a strong connection between two significant bodies of machine learning research: continual learning and sequence modeling. That is, we propose to formulate continual learning as a sequence modeling problem, allowing advanced sequence models to be utilized for continual learning. Under this formulation, the continual learning process becomes the forward pass of a sequence model. By adopting the meta-continual learning (MCL) framework, we can train the sequence model at the meta-level, on multiple continual learning episodes. As a specific example of our new formulation, we demonstrate the application of Transformers and their efficient variants as MCL methods. Our experiments on seven benchmarks, covering both classification and regression, show that sequence models can be an attractive solution for general MCL.
A Decoupled Multi-Task Network for Shadow Removal
Authors: Jiawei Liu, Qiang Wang, Huijie Fan, Wentao Li, Liangqiong Qu, Yandong Tang
Date: 2023-03-03 00:00:00
Proceeding: IEEE Transactions on Multimedia 2023 3
Tasks: Image Reconstruction, Image Shadow Removal, Shadow Removal

Shadow removal, which aims to restore the illumination in shadow regions, is challenging due to the diversity of shadows in terms of location, intensity, shape, and size. Different from most multi-task methods, which design elaborate multi-branch or multi-stage structures for better shadow removal, we introduce feature decomposition to learn better feature representations. Specifically, we propose a single-stage and decoupled multi-task network (DMTN) to explicitly learn the decomposed features for shadow removal, shadow matte estimation, and shadow image reconstruction. First, we propose several coarse-to-fine semi-convolution (SMC) modules to capture features sufficient for joint learning of these three tasks. Second, we design a theoretically supported feature decoupling layer to explicitly decouple the learned features into shadow image features and shadow matte features via weight reassignment. Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions. With multi-task collaboration, DMTN effectively recovers the illumination in shadow areas while ensuring the fidelity of non-shadow areas. Experimental results show that DMTN competes favorably with state-of-the-art multi-branch/multi-stage shadow removal methods, while maintaining the simplicity of single-stage methods. We have released our code to encourage future exploration in powerful feature representation for shadow removal https://github.com/nachifur/DMTN
A Shadow Imaging Bilinear Model and Three-branch Residual Network for Shadow Removal
Authors: Jiawei Liu, Qiang Wang, Huijie Fan, Jiandong Tian, Yandong Tang
Date: 2023-08-02 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2023 8
Tasks: Image Reconstruction, Image Shadow Removal, Shadow Removal

The current shadow removal pipeline relies on the detected shadow masks, which have limitations for penumbras and tiny shadows, and results in an excessively long pipeline. To address these issues, we propose a shadow imaging bilinear model and design a novel three-branch residual (TBR) network for shadow removal. Our bilinear model reveals the single-image shadow removal process and can explain why simply increasing the brightness of shadow areas cannot remove shadows without artifacts. We considerably shorten the shadow removal pipeline by modeling illumination compensation and developing a single-stage shadow removal network without additional detection and refinement networks. Specifically, our network consists of three task branches, i.e., shadow image reconstruction, shadow matte estimation, and shadow removal. To merge these three branches and enhance the shadow removal branch, we design a model-based TBR module. Multiple TBR modules are cascaded to generate an intensive information flow and facilitate feature integration among the three branches. Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. The model and code are available at https://github.com/nachifur/TBRNet.
Efficient Pattern-based Static Analysis Approach via Regular-Expression Rules
Authors: Xiaowen Zhang, Ying Zhou, Shin Hwei Tan
Date: 2023-05-15 00:00:00
Proceeding: 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) 2023 5

Pattern-based static analyzers like SpotBugs use bug patterns (rules) to detect bugs may have several limitations: (1) too slow, (2) do not usually support analysis of partial programs, (3) require parsing code into AST/CFG, and (4) high false positive rate. Each pattern relies on analysis context (e.g., data flow analysis) to improve the accuracy of the analysis. To understand the analysis contexts required by each pattern, we study the design of bug patterns in SpotBugs. Based on our study, we present Codegex, an efficient pattern-based static analysis approach that uses regular expression with several strategies to extract more information from program texts (syntax and type information). It can analyze partial and complete code quickly without parsing code into AST. We evaluate Codegex using two settings. First, we compare the effectiveness and efficiency of Codegex and SpotBugs in analyzing 52 projects. Our results show that Codegex can detect bugs with comparable accuracy as SpotBugs but up to 590X faster, showing the potential of using Codegex as the fast stage of SpotBugs in a two-stage approach for instant feedback. Second, we evaluate Codegex in automated code review by running it on 4256 PRs where it generated 372 review comments and received 116 feedback. Overall, 78.45% of the feedback that we received is positive, indicating the promise of using Codegex for automated code review.
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Authors: Seungyong Moon, Junyoung Yeom, Bumsoo Park, Hyun Oh Song
Date: 2023-07-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Contrastive Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge. This requires an agent to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods have been built upon model-based or hierarchical approaches, with the belief that an explicit module for long-term planning would be advantageous for learning hierarchical dependencies. However, these methods demand an excessive number of environment interactions or large model sizes, limiting their practicality. In this work, we demonstrate that proximal policy optimization (PPO), a simple yet versatile model-free algorithm, outperforms previous methods when optimized with recent implementation practices. Moreover, we find that the PPO agent can predict the next achievement to be unlocked to some extent, albeit with limited confidence. Based on this observation, we introduce a novel contrastive learning method, called achievement distillation, which strengthens the agent's ability to predict the next achievement. Our method exhibits a strong capacity for discovering hierarchical achievements and shows state-of-the-art performance on the challenging Crafter environment in a sample-efficient manner while utilizing fewer model parameters.
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model
Authors: Guangming Liu, Qi Liu, Jing Liang
Date: 2023-12-08 00:00:00
Tasks: Denoising, global-optimization, Image Segmentation, model, Semantic Segmentation

In this paper, we propose a novel locally statistical active contour model (LACM) based on Aubert-Aujol (AA) denoising model and variational level set method, which can be used for SAR images segmentation with intensity inhomogeneity. Then we transform the proposed model into a global optimization model by using convex relaxation technique. Firstly, we apply the Split Bregman technique to transform the global optimization model into two alternating optimization processes of Shrink operator and Laplace operator, which is called SB_LACM model. Moreover, we propose two fast models to solve the global optimization model , which are more efficient than the SB_LACM model. The first model is: we add the proximal function to transform the global optimization model to a general ROF model[29], which can be solved by a fast denoising algorithm proposed by R.-Q.Jia, and H.Zhao; Thus we obtain a fast segmentation algorithm with global optimization solver that does not involve partial differential equations or difference equation, and only need simple difference computation. The second model is: we use a different splitting approach than one model to transform the global optimization model into a differentiable term and a general ROF model term, which can be solved by the same technique as the first model. Experiments using some challenging synthetic images and Envisat SAR images demonstrate the superiority of our proposed models with respect to the state-of-the-art models.
I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions
Authors: Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan, Junye Wang, Jingyi Yu, Jingya Wang, Lan Xu
Date: 2023-12-10 00:00:00
Proceeding: CVPR 2024 1
Tasks: Human-Object Interaction Detection, Object, Object Tracking

We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper, we present I'm-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former, we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter, we tailor a category-aware motion diffusion model, which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body, hand, and object motions. Moreover, we contribute a large dataset with ground truth human and object motions, dense RGB inputs, and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.
VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data
Authors: Jian Shi, Peter Wonka
Date: 2023-12-11 00:00:00
Tasks: 3D Human Pose Estimation, Keypoint Detection, Keypoint Estimation

We present \textit{VoxelKP}, a novel fully sparse network architecture tailored for human keypoint estimation in LiDAR data. The key challenge is that objects are distributed sparsely in 3D space, while human keypoint detection requires detailed local information wherever humans are present. We propose four novel ideas in this paper. First, we propose sparse selective kernels to capture multi-scale context. Second, we introduce sparse box-attention to focus on learning spatial correlations between keypoints within each human instance. Third, we incorporate a spatial encoding to leverage absolute 3D coordinates when projecting 3D voxels to a 2D grid encoding a bird's eye view. Finally, we propose hybrid feature learning to combine the processing of per-voxel features with sparse convolution. We evaluate our method on the Waymo dataset and achieve an improvement of $27\%$ on the MPJPE metric compared to the state-of-the-art, \textit{HUM3DIL}, trained on the same data, and $12\%$ against the state-of-the-art, \textit{GC-KPL}, pretrained on a $25\times$ larger dataset. To the best of our knowledge, \textit{VoxelKP} is the first single-staged, fully sparse network that is specifically designed for addressing the challenging task of 3D keypoint estimation from LiDAR data, achieving state-of-the-art performances. Our code is available at \url{https://github.com/shijianjian/VoxelKP}.