pwc-archive/papers-with-abstracts huggingface dataset:
Universal Novelty Detection Through Adaptive Contrastive Learning
Authors: Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Sabokrou, Mohammad Hossein Rohban
Date: 2024-08-20 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Novelty Detection

Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpreted as generalization across various distributions of training or test data. More precisely, for novelty detection, distribution shifts may occur in the training set or the test set. Shifts in the training set refer to cases where we train a novelty detector on a new dataset and expect strong transferability. Conversely, distribution shifts in the test set indicate the methods' performance when the trained model encounters a shifted test sample. We experimentally show that existing methods falter in maintaining universality, which stems from their rigid inductive biases. Motivated by this, we aim for more generalized techniques that have more adaptable inductive biases. In this context, we leverage the fact that contrastive learning provides an efficient framework to easily switch and adapt to new inductive biases through the proper choice of augmentations in forming the negative pairs. We propose a novel probabilistic auto-negative pair generation method AutoAugOOD, along with contrastive learning, to yield a universal novelty detector method. Our experiments demonstrate the superiority of our method under different distribution shifts in various image benchmark datasets. Notably, our method emerges universality in the lens of adaptability to different setups of novelty detection, including one-class, unlabeled multi-class, and labeled multi-class settings. Code: https://github.com/mojtaba-nafez/UNODE
A Lightweight Modular Framework for Low-Cost Open-Vocabulary Object Detection Training
Authors: Bilal Faye, Binta Sow, Hanane Azzag, Mustapha Lebbah
Date: 2024-08-20 00:00:00
Tasks: Autonomous Vehicles, Computational Efficiency, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection, Phrase Grounding, Referring Expression, Referring Expression Comprehension

Object detection is a fundamental challenge in computer vision, centered on recognizing objects within images, with diverse applications in areas like image analysis, robotics, and autonomous vehicles. Although existing methods have achieved great success, they are often constrained by a fixed vocabulary of objects. To overcome this limitation, approaches like MDETR have redefined object detection by incorporating region-level vision-language pre-training, enabling open-vocabulary object detectors. However, these methods are computationally heavy due to the simultaneous training of large models for both vision and language representations. To address this, we introduce a lightweight framework that significantly reduces the number of parameters while preserving, or even improving, performance. Our solution is applied to MDETR, resulting in the development of Lightweight MDETR (LightMDETR), an optimized version of MDETR designed to enhance computational efficiency without sacrificing accuracy. The core of our approach involves freezing the MDETR backbone and training only the Universal Projection module (UP), which bridges vision and language representations. A learnable modality token parameter allows the UP to seamlessly switch between modalities. Evaluations on tasks like phrase grounding, referring expression comprehension, and segmentation show that LightMDETR not only reduces computational costs but also outperforms several state-of-the-art methods in terms of accuracy.
Generative AI in Industrial Machine Vision -- A Review
Authors: Hans Aoyang Zhou, Dominik Wolfschläger, Constantinos Florides, Jonas Werheid, Hannes Behnen, Jan-Henrick Woltersmann, Tiago C. Pinto, Marco Kemmerling, Anas Abdelrazeq, Robert H. Schmitt
Date: 2024-08-20 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

Machine vision enhances automation, quality control, and operational efficiency in industrial applications by enabling machines to interpret and act on visual data. While traditional computer vision algorithms and approaches remain widely utilized, machine learning has become pivotal in current research activities. In particular, generative AI demonstrates promising potential by improving pattern recognition capabilities, through data augmentation, increasing image resolution, and identifying anomalies for quality control. However, the application of generative AI in machine vision is still in its early stages due to challenges in data diversity, computational requirements, and the necessity for robust validation methods. A comprehensive literature review is essential to understand the current state of generative AI in industrial machine vision, focusing on recent advancements, applications, and research trends. Thus, a literature review based on the PRISMA guidelines was conducted, analyzing over 1,200 papers on generative AI in industrial machine vision. Our findings reveal various patterns in current research, with the primary use of generative AI being data augmentation, for machine vision tasks such as classification and object detection. Furthermore, we gather a collection of application challenges together with data requirements to enable a successful application of generative AI in industrial machine vision. This overview aims to provide researchers with insights into the different areas and applications within current research, highlighting significant advancements and identifying opportunities for future work.
Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations
Authors: Yifu Han, Francois P. Hamon, Louis J. Durlofsky
Date: 2024-08-20 00:00:00

Deep learning surrogate modeling shows great promise for subsurface flow applications, but the training demands can be substantial. Here we introduce a new surrogate modeling framework to predict CO2 saturation, pressure and surface displacement for use in the history matching of carbon storage operations. Rather than train using a large number of expensive coupled flow-geomechanics simulation runs, training here involves a large number of inexpensive flow-only simulations combined with a much smaller number of coupled runs. The flow-only runs use an effective rock compressibility, which is shown to provide accurate predictions for saturation and pressure for our system. A recurrent residual U-Net architecture is applied for the saturation and pressure surrogate models, while a new residual U-Net model is introduced to predict surface displacement. The surface displacement surrogate accepts, as inputs, geomodel quantities along with saturation and pressure surrogate predictions. Median relative error for a diverse test set is less than 4% for all variables. The surrogate models are incorporated into a hierarchical Markov chain Monte Carlo history matching workflow. Surrogate error is included using a new treatment involving the full model error covariance matrix. A high degree of prior uncertainty, with geomodels characterized by uncertain geological scenario parameters (metaparameters) and associated realizations, is considered. History matching results for a synthetic true model are generated using in-situ monitoring-well data only, surface displacement data only, and both data types. The enhanced uncertainty reduction achieved with both data types is quantified. Posterior saturation and surface displacement fields are shown to correspond well with the true solution.
HMoE: Heterogeneous Mixture of Experts for Language Modeling
Authors: An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, J. N. Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, Cheng-Zhong Xu
Date: 2024-08-20 00:00:00
Tasks: Computational Efficiency, Language Modeling, Language Modelling, Mixture-of-Experts

Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE), where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, enhancing computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves lower loss with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance.
Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning
Authors: Dong Geun Shin, Hye Won Chung
Date: 2024-08-20 00:00:00
Tasks: Classification, Long-tail Learning, Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples is a critical task for reliable machine learning. However, it becomes particularly challenging when the models are trained on long-tailed datasets, as the models often struggle to distinguish tail-class in-distribution samples from OOD samples. We examine the main challenges in this problem by identifying the trade-offs between OOD detection and in-distribution (ID) classification, faced by existing methods. We then introduce our method, called \textit{Representation Norm Amplification} (RNA), which solves this challenge by decoupling the two problems. The main idea is to use the norm of the representation as a new dimension for OOD detection, and to develop a training method that generates a noticeable discrepancy in the representation norm between ID and OOD data, while not perturbing the feature learning for ID classification. Our experiments show that RNA achieves superior performance in both OOD detection and classification compared to the state-of-the-art methods, by 1.70\% and 9.46\% in FPR95 and 2.43\% and 6.87\% in classification accuracy on CIFAR10-LT and ImageNet-LT, respectively. The code for this work is available at https://github.com/dgshin21/RNA.
Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
Authors: Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong
Date: 2024-08-20 00:00:00

Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost. This advancement marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. The source code of NeurELA can be accessed at https://github.com/GMC-DRL/Neur-ELA.
End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
Authors: Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger, Andre Kaup
Date: 2024-08-20 00:00:00
Tasks: Attribute

Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
Authors: Mirko Nardi, Lorenzo Valerio, Andrea Passarella
Date: 2024-08-20 00:00:00
Tasks: Clustering, Federated Learning

Federated Learning (FL) is a pivotal approach in decentralized machine learning, especially when data privacy is crucial and direct data sharing is impractical. While FL is typically associated with supervised learning, its potential in unsupervised scenarios is underexplored. This paper introduces a novel unsupervised federated learning methodology designed to identify the complete set of categories (global K) across multiple clients within label-free, non-uniform data distributions, a process known as Federated Clustering. Our approach, Federated Cluster-Wise Refinement (FedCRef), involves clients that collaboratively train models on clusters with similar data distributions. Initially, clients with diverse local data distributions (local K) train models on their clusters to generate compressed data representations. These local models are then shared across the network, enabling clients to compare them through reconstruction error analysis, leading to the formation of federated groups.In these groups, clients collaboratively train a shared model representing each data distribution, while continuously refining their local clusters to enhance data association accuracy. This iterative process allows our system to identify all potential data distributions across the network and develop robust representation models for each. To validate our approach, we compare it with traditional centralized methods, establishing a performance baseline and showcasing the advantages of our distributed solution. We also conduct experiments on the EMNIST and KMNIST datasets, demonstrating FedCRef's ability to refine and align cluster models with actual data distributions, significantly improving data representation precision in unsupervised federated settings.
CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
Authors: YuTing Liu, Jinghao Zhang, Yizhou Dang, Yuliang Liang, Qiang Liu, Guibing Guo, Jianzhe Zhao, Xingwei Wang
Date: 2024-08-20 00:00:00
Tasks: Collaborative Filtering, General Knowledge, World Knowledge

Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input and then fine-tuning to align these features with LLM's input space. Although effective, in this work, we identify two limitations when adapting LLMs to recommendation tasks, which hinder the integration of general knowledge and collaborative information, resulting in sub-optimal recommendation performance. (1) Fine-tuning LLM with recommendation data can undermine its inherent world knowledge and fundamental competencies, which are crucial for interpreting and inferring recommendation text. (2) Incorporating collaborative features into textual prompts disrupts the semantics of the original prompts, preventing LLM from generating appropriate outputs. In this paper, we propose a new paradigm, \textbf{Co}llaborative \textbf{Lo}RA (CoRA), with a collaborative query generator. Rather than input space alignment, this method aligns collaborative information with LLM's parameter space, representing them as incremental weights to update LLM's output. This way, LLM perceives collaborative information without altering its general knowledge and text inference capabilities. Specifically, we employ a collaborative filtering model to extract user and item embeddings and inject them into a set number of learnable queries. We then convert collaborative queries into collaborative weights with low-rank properties and merge the collaborative weights into LLM's weights, enabling LLM to perceive the collaborative signals and generate personalized recommendations without fine-tuning or extra collaborative tokens in prompts. Extensive experiments confirm that CoRA effectively integrates collaborative information into LLM, enhancing recommendation performance.
Interactive Counterfactual Generation for Univariate Time Series
Authors: Udo Schlegel, Julius Rauscher, Daniel A. Keim
Date: 2024-08-20 00:00:00
Tasks: counterfactual, Time Series, Time Series Classification

We propose an interactive methodology for generating counterfactual explanations for univariate time series data in classification tasks by leveraging 2D projections and decision boundary maps to tackle interpretability challenges. Our approach aims to enhance the transparency and understanding of deep learning models' decision processes. The application simplifies the time series data analysis by enabling users to interactively manipulate projected data points, providing intuitive insights through inverse projection techniques. By abstracting user interactions with the projected data points rather than the raw time series data, our method facilitates an intuitive generation of counterfactual explanations. This approach allows for a more straightforward exploration of univariate time series data, enabling users to manipulate data points to comprehend potential outcomes of hypothetical scenarios. We validate this method using the ECG5000 benchmark dataset, demonstrating significant improvements in interpretability and user understanding of time series classification. The results indicate a promising direction for enhancing explainable AI, with potential applications in various domains requiring transparent and interpretable deep learning models. Future work will explore the scalability of this method to multivariate time series data and its integration with other interpretability techniques.
On the Approximability of Stationary Processes using the ARMA Model
Authors: Anand Ganesh, Babhrubahan Bose, Anand Rajagopalan
Date: 2024-08-20 00:00:00
Tasks: LEMMA

Within the theoretical literature on stationary random variables, pure Moving Average models and pure Autoregressive models have a rich body of work, but the corresponding literature on Autoregressive Moving Average (ARMA) models is very sparse. We attempt to fill certain gaps in this sparse line of work. Central to our observations is the spectral lemma connecting supnorm based function approximation on the unit circle to random variable approximation. This method allows us to provide quantitative approximation bounds in contrast with the qualitative boundedness and stability guarantees associated with unit root tests. Using the spectral lemma we first identify a class of stationary processes where approximation guarantees are feasible. This turns a known heuristic argument motivating ARMA models based on rational approximations into a rigorous result. Second, we identify an idealized stationary random process for which we conjecture that a good ARMA approximation is not possible. Third, we calculate exact approximation bounds for an example process, and a constructive proof that, for a given order, Pad\'e approximations do not always correspond to the best ARMA approximation. Unlike prior literature, our approach uses the generating function of the random process rather than the spectral measure, and further our results focus on approximation error of the random variable rather than the prediction error as in some classical infimum results by Szego, Kolmogorov, and Wiener.
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Authors: Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Zichao Yan, Rory Stark, Kun Zhang, Thore Graepel
Date: 2024-08-20 00:00:00
Tasks: Benchmarking

We introduce a comprehensive framework for perturbation response modeling in single cells, aimed at standardizing benchmarking in this rapidly evolving field. Our approach includes a modular and user-friendly model development and evaluation platform, a collection of diverse perturbational datasets, and a set of metrics designed to fairly compare models and dissect their performance nuances. Through extensive evaluation of both published and baseline models across diverse datasets, we highlight the limitations of widely used models, such as mode collapse. We also demonstrate the importance of rank metrics which complement traditional model fit measures, such as RMSE, for validating model effectiveness. Notably, our results show that while no single model architecture clearly outperforms others, simpler architectures are generally competitive and scale well with larger datasets. Overall, this benchmarking exercise sets new standards for model evaluation, supports robust model development, and advances the potential of these models to use high-throughput genetic and chemical screens for disease target discovery.
GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction
Authors: Shengxiang Hu, Guobing Zou, Bofeng Zhang, Shaogang Wu, Shiyi Lin, Yanglan Gan, Yixin Chen
Date: 2024-08-20 00:00:00
Tasks: Graph Attention, Prediction

Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%.