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Title: Non-stationary Bandits with Knapsacks Abstract: In this paper, we study the problem of bandits with knapsacks (BwK) in a non-stationary environment. The BwK problem generalizes the multi-arm bandit (MAB) problem to model the resource consumption associated with playing each arm. At each time, the decision maker/... |
Title: Uniform Generalization Bound on Time and Inverse Temperature for Gradient Descent Algorithm and its Application to Analysis of Simulated Annealing Abstract: In this paper, we propose a novel uniform generalization bound on the time and inverse temperature for stochastic gradient Langevin dynamics (SGLD) in a non... |
Title: VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection Abstract: This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ proje... |
Title: Deletion and Insertion Tests in Regression Models Abstract: A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function $f$. The insertion and deletion tests of \cite{petsiuk2018rise} are used to judge the quality of algorithms that rank pixels... |
Title: Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling Abstract: We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modes... |
Title: Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret Abstract: We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on explorati... |
Title: Differentially Private AUC Computation in Vertical Federated Learning Abstract: Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federated learning (vFL) focuses on the scenario where feat... |
Title: Linear Connectivity Reveals Generalization Strategies Abstract: It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintained. Under some circumstances... |
Title: AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models Abstract: Fine-tuning large-scale pre-trained language models to downstream tasks require updating hundreds of millions of parameters. This not only increases the serving cost to store a large copy of the model weights for every t... |
Title: Convolutional Neural Processes for Inpainting Satellite Images Abstract: The widespread availability of satellite images has allowed researchers to model complex systems such as disease dynamics. However, many satellite images have missing values due to measurement defects, which render them unusable without dat... |
Title: Multi-Head Online Learning for Delayed Feedback Modeling Abstract: In online advertising, it is highly important to predict the probability and the value of a conversion (e.g., a purchase). It not only impacts user experience by showing relevant ads, but also affects ROI of advertisers and revenue of marketplace... |
Title: Reward Uncertainty for Exploration in Preference-based Reinforcement Learning Abstract: Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by activel... |
Title: Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word Abstract: In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic generative model that represents the symbol emer... |
Title: Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT Abstract: We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model. The Sparse Mixer slightly outperforms (<1%) BERT on GLUE and... |
Title: Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks Abstract: Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the combined effect of multi-modal informatio... |
Title: PLAtE: A Large-scale Dataset for List Page Web Extraction Abstract: Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites. However, a barrier for continued progress is the small number of datasets large enough to train these m... |
Title: RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation Abstract: Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order... |
Title: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization Abstract: How can we train an assistive human-machine interface (e.g., an electromyography-based limb prosthesis) to translate a user's raw command signals into the actions of a robot or computer when there is no prior ma... |
Title: Imposing Gaussian Pre-Activations in a Neural Network Abstract: The goal of the present work is to propose a way to modify both the initialization distribution of the weights of a neural network and its activation function, such that all pre-activations are Gaussian. We propose a family of pairs initialization/a... |
Title: Hardness of Maximum Likelihood Learning of DPPs Abstract: Determinantal Point Processes (DPPs) are a widely used probabilistic model for negatively correlated sets. DPPs have been successfully employed in Machine Learning applications to select a diverse, yet representative subset of data. In seminal work on DPP... |
Title: Learning to Model Editing Processes Abstract: Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based model... |
Title: TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models Abstract: We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework. We provide an efficient method to calculate the NTK of multilayer perceptrons... |
Title: Low-rank Optimal Transport: Approximation, Statistics and Debiasing Abstract: The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e.g. single-cell genomics) or used t... |
Title: Wavelet Feature Maps Compression for Image-to-Image CNNs Abstract: Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well ... |
Title: Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning Abstract: We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a lim... |
Title: K-12BERT: BERT for K-12 education Abstract: Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to s... |
Title: Certified Robustness Against Natural Language Attacks by Causal Intervention Abstract: Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples. This paper follows a causal perspective to look into the adversarial vulnerability and proposes Causal Intervent... |
Title: Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy Abstract: Among the various aspects of algorithmic fairness studied in recent years, the tension between satisfying both \textit{sufficiency} and \textit{separation} -- e.g. the ratios of positive or negative predictive values, and false positi... |
Title: ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases Abstract: Low-quality listings and bad actor behavior in online retail websites threatens e-commerce business as these result in sub-optimal buying experience and erode customer trust. When a new listing is creat... |
Title: Fast & Furious: Modelling Malware Detection as Evolving Data Streams Abstract: Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been mo... |
Title: FreDo: Frequency Domain-based Long-Term Time Series Forecasting Abstract: The ability to forecast far into the future is highly beneficial to many applications, including but not limited to climatology, energy consumption, and logistics. However, due to noise or measurement error, it is questionable how far into... |
Title: lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents Abstract: Recent advances have shown that SNN-based systems can efficiently perform unsupervised continual learning due to their bio-plausible learning rule, e.g., Spike-Timing-... |
Title: Policy Compliance Detection via Expression Tree Inference Abstract: Policy Compliance Detection (PCD) is a task we encounter when reasoning over texts, e.g. legal frameworks. Previous work to address PCD relies heavily on modeling the task as a special case of Recognizing Textual Entailment. Entailment is applic... |
Title: History Compression via Language Models in Reinforcement Learning Abstract: In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history represent... |
Title: Interpretation Quality Score for Measuring the Quality of interpretability methods Abstract: Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their de... |
Title: Taming the sign problem of explicitly antisymmetrized neural networks via rough activation functions Abstract: Explicit antisymmetrization of a two-layer neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. How... |
Title: RevUp: Revise and Update Information Bottleneck for Event Representation Abstract: In machine learning, latent variables play a key role to capture the underlying structure of data, but they are often unsupervised. When we have side knowledge that already has high-level information about the input data, we can u... |
Title: Asynchronous Neural Networks for Learning in Graphs Abstract: This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate their neighbors in each round, whi... |
Title: EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling Abstract: This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories. MCMC trajectories of different lengths correspond to models with different purposes. Our exp... |
Title: Gacs-Korner Common Information Variational Autoencoder Abstract: We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each. Our notion of common information is a variational relaxation... |
Title: Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model Abstract: Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement ex... |
Title: Forecasting Multilinear Data via Transform-Based Tensor Autoregression Abstract: In the era of big data, there is an increasing demand for new methods for analyzing and forecasting 2-dimensional data. The current research aims to accomplish these goals through the combination of time-series modeling and multilin... |
Title: Rethinking Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization Abstract: Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).... |
Title: Learning for Expressive Task-Related Sentence Representations Abstract: NLP models learn sentence representations for downstream tasks by tuning a model which is pre-trained by masked language modeling. However, after tuning, the learned sentence representations may be skewed heavily toward label space and thus ... |
Title: Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning Abstract: Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem o... |
Title: Regret-Aware Black-Box Optimization with Natural Gradients, Trust-Regions and Entropy Control Abstract: Most successful stochastic black-box optimizers, such as CMA-ES, use rankings of the individual samples to obtain a new search distribution. Yet, the use of rankings also introduces several issues such as the ... |
Title: Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums Abstract: Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees conver... |
Title: D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion Abstract: Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral... |
Title: Not too little, not too much: a theoretical analysis of graph (over)smoothing Abstract: We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the average of the features of its neighbors. Indeed, it has quickly been observed that Graph Neural Networks (GNNs), which genera... |
Title: Mathematical Models of Human Drivers Using Artificial Risk Fields Abstract: In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. A risk field assigns a non-negative risk measure to the state of the system in order to... |
Title: One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks Abstract: Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Error-minimizing noise, which is injected to clean data, is one of the most successful methods for preventing DNNs from giving correct predic... |
Title: Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks Abstract: The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note th... |
Title: Inference of a Rumor's Source in the Independent Cascade Model Abstract: We consider the so-called Independent Cascade Model for rumor spreading or epidemic processes popularized by Kempe et al.\ [2003]. In this model, a small subset of nodes from a network are the source of a rumor. In discrete time steps, each... |
Title: Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Classification Abstract: Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and classification difficulty imbalance between different classes. In order to alleviate the problem of datas... |
Title: Federated singular value decomposition for high dimensional data Abstract: Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institution... |
Title: Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width Abstract: Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021), we mak... |
Title: Associative Learning Mechanism for Drug-Target Interaction Prediction Abstract: As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interactio... |
Title: DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks Abstract: Deep learning is attracting interest across a variety of domains, including natural language processing, speech recognition, and computer vision. However, model training is time-consuming and requires huge computational r... |
Title: Bias Discovery in Machine Learning Models for Mental Health Abstract: Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model train... |
Title: Optimality Conditions and Algorithms for Top-K Arm Identification Abstract: We consider the top-k arm identification problem for multi-armed bandits with rewards belonging to a one-parameter canonical exponential family. The objective is to select the set of k arms with the highest mean rewards by sequential all... |
Title: Ensemble Multi-Relational Graph Neural Networks Abstract: It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able ... |
Title: Deep Reinforcement Learning for Multi-class Imbalanced Training Abstract: With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority cla... |
Title: Boosting Tail Neural Network for Realtime Custom Keyword Spotting Abstract: In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited com... |
Title: On statistic alignment for domain adaptation in structural health monitoring Abstract: The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adaptation (DA) - gives rise to the possibility of l... |
Title: FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment Abstract: Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, ... |
Title: Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance Abstract: With the rapid development of the Metaverse, virtual humans have emerged, and human image synthesis and editing techniques, such as pose transfer, have recently become popular. Most of the existing techni... |
Title: PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization Abstract: Learning neural networks using only a small amount of data is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a regularizer for the variational modeling of inverse prob... |
Title: Concurrent Credit Assignment for Data-efficient Reinforcement Learning Abstract: The capability to widely sample the state and action spaces is a key ingredient toward building effective reinforcement learning algorithms. The variational optimization principles exposed in this paper emphasize the importance of a... |
Title: Naive Few-Shot Learning: Sequence Consistency Evaluation Abstract: Cognitive psychologists often use the term $\textit{fluid intelligence}$ to describe the ability of humans to solve novel tasks without any prior training. In contrast to humans, deep neural networks can perform cognitive tasks only after extensi... |
Title: Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents Abstract: In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network... |
Title: Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs Abstract: This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new ... |
Title: Learning Stabilizing Policies in Stochastic Control Systems Abstract: In this work, we address the problem of learning provably stable neural network policies for stochastic control systems. While recent work has demonstrated the feasibility of certifying given policies using martingale theory, the problem of ho... |
Title: Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings Abstract: In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems. This embedding preserves input-outpu... |
Title: The Data-Production Dispositif Abstract: Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platfo... |
Title: Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control Abstract: Most machine learning methods depend on the tuning of hyper-parameters. For kernel ridge regression (KRR) with the Gaussian kernel, the hyper-parameter is the bandwidth. The bandwidth specifies the length-scale of the kernel ... |
Title: 3D helical CT reconstruction with memory efficient invertible Learned Primal-Dual method Abstract: Helical acquisition geometry is the most common geometry used in computed tomography (CT) scanners for medical imaging. We adapt the invertible Learned Primal-Dual (iLPD) deep neural network architecture so that it... |
Title: Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks Abstract: Echo State Networks (ESN) are versatile recurrent neural network models in which the hidden layer remains unaltered during training. Interactions among nodes of this static backbone produce diverse representations... |
Title: Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production Abstract: The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from ... |
Title: Pynblint: a Static Analyzer for Python Jupyter Notebooks Abstract: Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows. The notebook format, however, has been criticized for inducing bad programming practices; indeed, researchers have already shown that open-source ... |
Title: How Human is Human Evaluation? Improving the Gold Standard for NLG with Utility Theory Abstract: Human ratings are treated as the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and then rank NLG systems by their average scores. However, ... |
Title: Compression-aware Training of Neural Networks using Frank-Wolfe Abstract: Many existing Neural Network pruning approaches either rely on retraining to compensate for pruning-caused performance degradation or they induce strong biases to converge to a specific sparse solution throughout training. A third paradigm... |
Title: Large Language Models are Zero-Shot Reasoners Abstract: Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique fo... |
Title: An Adaptive Contrastive Learning Model for Spike Sorting Abstract: Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field potentials is sufficient for d... |
Title: Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision Abstract: Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constru... |
Title: Physics-Embedded Neural Networks: $\boldsymbol{\mathrm{E}(n)}$-Equivariant Graph Neural PDE Solvers Abstract: Graph neural network (GNN) is a promising approach to learning and predicting physical phenomena described in boundary value problems, such as partial differential equations (PDEs) with boundary conditio... |
Title: An interpretation of the final fully connected layer Abstract: In recent years neural networks have achieved state-of-the-art accuracy for various tasks but the the interpretation of the generated outputs still remains difficult. In this work we attempt to provide a method to understand the learnt weights in the... |
Title: Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Abstract: We study for the first time uncertainty-aware modeling of continuous-time dynamics of interacting objects. We introduce a new model that decomposes independent dynamics of single objects accurately from their interactions. By empl... |
Title: A Quadrature Rule combining Control Variates and Adaptive Importance Sampling Abstract: Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the di... |
Title: Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning Abstract: Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectiv... |
Title: Faithful Explanations for Deep Graph Models Abstract: This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations for GNNs. It applies to existing explanation methods, including feature attribut... |
Title: CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing Abstract: Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) ... |
Title: Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model Abstract: Ensembling is a popular method used to improve performance as a last resort. However, ensembling multiple models finetuned from a single pretrained model has been not very effective; this could be due to the lack of diversity among e... |
Title: Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization Abstract: We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acqu... |
Title: Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free Abstract: Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular trigger. Several works attempt to detect whether a given... |
Title: Penalized Proximal Policy Optimization for Safe Reinforcement Learning Abstract: Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard const... |
Title: Learning to Assemble Geometric Shapes Abstract: Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style pa... |
Title: NFL: Robust Learned Index via Distribution Transformation Abstract: Recent works on learned index open a new direction for the indexing field. The key insight of the learned index is to approximate the mapping between keys and positions with piece-wise linear functions. Such methods require partitioning key spac... |
Title: SepIt: Approaching a Single Channel Speech Separation Bound Abstract: We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech. Using the bound, we are able to show that while the recent methods have made significant... |
Title: G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection Abstract: Arbitrary-oriented object representations contain the oriented bounding box (OBB), quadrilateral bounding box (QBB), and point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as t... |
Title: DPSNN: A Differentially Private Spiking Neural Network Abstract: Privacy-preserving is a key problem for the machine learning algorithm. Spiking neural network (SNN) plays an important role in many domains, such as image classification, object detection, and speech recognition, but the study on the privacy prote... |
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