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Title: Tackling Multiple Tasks with One Single Learning Framework Abstract: Deep Multi-Task Learning (DMTL) has been widely studied in the machine learning community and applied to a broad range of real-world applications. Searching for the optimal knowledge sharing in DMTL is more challenging for sequential learning p... |
Title: L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library Abstract: Despite being the third most popular language in India, the Marathi language lacks useful NLP resources. Moreover, popular NLP libraries do not have support for the Marathi language. With L3Cube-MahaNLP, we aim to build r... |
Title: What are People Talking about in #BackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model Abstract: Minority groups have been using social media to organize social movements that create profound social impacts... |
Title: Heterogeneous Treatment Effects Estimation: When Machine Learning meets multiple treatment regime Abstract: In many scientific and engineering domains, inferring the effect of treatment and exploring its heterogeneity is crucial for optimization and decision making. In addition to Machine Learning based models (... |
Title: Evaluating Automated Driving Planner Robustness against Adversarial Influence Abstract: Evaluating the robustness of automated driving planners is a critical and challenging task. Although methodologies to evaluate vehicles are well established, they do not yet account for a reality in which vehicles with autono... |
Title: Learning Security Strategies through Game Play and Optimal Stopping Abstract: We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve ... |
Title: Generalization bounds and algorithms for estimating conditional average treatment effect of dosage Abstract: We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying... |
Title: On the Robustness of Safe Reinforcement Learning under Observational Perturbations Abstract: Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL ... |
Title: The impact of memory on learning sequence-to-sequence tasks Abstract: The recent success of neural networks in machine translation and other fields has drawn renewed attention to learning sequence-to-sequence (seq2seq) tasks. While there exists a rich literature that studies classification and regression using s... |
Title: Context-based Virtual Adversarial Training for Text Classification with Noisy Labels Abstract: Deep neural networks (DNNs) have a high capacity to completely memorize noisy labels given sufficient training time, and its memorization, unfortunately, leads to performance degradation. Recently, virtual adversarial ... |
Title: Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection Abstract: Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). However, most popular deep AD detectors cannot prot... |
Title: Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases Abstract: Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take... |
Title: COFS: Controllable Furniture layout Synthesis Abstract: Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation. Many existing methods tackle this problem as a sequence generation problem which imposes a spe... |
Title: Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation Abstract: Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based machine learning p... |
Title: Speaker Identification using Speech Recognition Abstract: The audio data is increasing day by day throughout the globe with the increase of telephonic conversations, video conferences and voice messages. This research provides a mechanism for identifying a speaker in an audio file, based on the human voice biome... |
Title: Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning Abstract: Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different languag... |
Title: Urdu News Article Recommendation Model using Natural Language Processing Techniques Abstract: There are several online newspapers in urdu but for the users it is difficult to find the content they are looking for because these most of them contain irrelevant data and most users did not get what they want to retr... |
Title: Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning Abstract: Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-perf... |
Title: Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) Abstract: In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Ph... |
Title: Continuous Generative Neural Networks Abstract: In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting. The architecture is inspired by DCGAN, with one fully connected layer, several convolutional layers and nonlinear activation funct... |
Title: Graph Structure Based Data Augmentation Method Abstract: In this paper, we propose a novel graph-based data augmentation method that can generally be applied to medical waveform data with graph structures. In the process of recording medical waveform data, such as electrocardiogram (ECG) or electroencephalogram ... |
Title: A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension Abstract: Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension. Despite the core role of sparse logistic regression in statistics and machine... |
Title: Do Residual Neural Networks discretize Neural Ordinary Differential Equations? Abstract: Neural Ordinary Differential Equations (Neural ODEs) are the continuous analog of Residual Neural Networks (ResNets). We investigate whether the discrete dynamics defined by a ResNet are close to the continuous one of a Neur... |
Title: Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction Abstract: Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the num... |
Title: An adaptive granularity clustering method based on hyper-ball Abstract: The purpose of cluster analysis is to classify elements according to their similarity. Its applications range from astronomy to bioinformatics and pattern recognition. Our method is based on the idea that the data with similar distribution f... |
Title: Joint Abductive and Inductive Neural Logical Reasoning Abstract: Neural logical reasoning (NLR) is a fundamental task in knowledge discovery and artificial intelligence. NLR aims at answering multi-hop queries with logical operations on structured knowledge bases based on distributed representations of queries a... |
Title: Independent and Decentralized Learning in Markov Potential Games Abstract: We propose a multi-agent reinforcement learning dynamics, and analyze its convergence properties in infinite-horizon discounted Markov potential games. We focus on the independent and decentralized setting, where players can only observe ... |
Title: Masked Distillation with Receptive Tokens Abstract: Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However, not every pixel contributes equally to the performance, and a good student shou... |
Title: Learning Locality and Isotropy in Dialogue Modeling Abstract: Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produc... |
Title: 3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction Abstract: Recently, the transformer model has been successfully employed for the multi-view 3D reconstruction problem. However, challenges remain on designing an attention mechanism to explore the multiview features and exploit their relations ... |
Title: Mean Field inference of CRFs based on GAT Abstract: In this paper we propose an improved mean-field inference algorithm for the fully connected paired CRFs model. The improved method Message Passing operation is changed from the original linear convolution to the present graph attention operation, while the proc... |
Title: No-regret Learning in Repeated First-Price Auctions with Budget Constraints Abstract: Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions. Although there has been a line of works concerning online bidding strategies in first-price auctions, it s... |
Title: Provable Benefits of Representational Transfer in Reinforcement Learning Abstract: We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a target task. We propos... |
Title: AutoDisc: Automatic Distillation Schedule for Large Language Model Compression Abstract: Driven by the teacher-student paradigm, knowledge distillation is one of the de facto ways for language model compression. Recent studies have uncovered that conventional distillation is less effective when facing a large ca... |
Title: Calibrated Predictive Distributions via Diagnostics for Conditional Coverage Abstract: Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. A large body of work (including normalizing flows and Bayesian neural networks) has been devoted to describing the entire predictive ... |
Title: Representation Gap in Deep Reinforcement Learning Abstract: Deep reinforcement learning gives the promise that an agent learns good policy from high-dimensional information. Whereas representation learning removes irrelevant and redundant information and retains pertinent information. We consider the representat... |
Title: A Model of One-Shot Generalization Abstract: We provide a theoretical framework to study a phenomenon that we call one-shot generalization. This phenomenon refers to the ability of an algorithm to perform transfer learning within a single task, meaning that it correctly classifies a test point that has a single ... |
Title: Machine Learning for Microcontroller-Class Hardware -- A Review Abstract: The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering ... |
Title: The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization Abstract: Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training. Recently, the technique of Inv... |
Title: Functional Linear Regression of CDFs Abstract: The estimation of cumulative distribution functions (CDF) is an important learning task with a great variety of downstream applications, e.g., risk assessments in predictions and decision making. We study functional regression of contextual CDFs where each data poin... |
Title: SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners Abstract: Self-supervised Masked Autoencoders (MAE) are emerging as a new pre-training paradigm in computer vision. MAE learns semantics implicitly via reconstructing local patches, requiring thousands of pre-training epochs to achieve favorabl... |
Title: Improving VAE-based Representation Learning Abstract: Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the representations learned by VAE are less competitive than other non-latent vari... |
Title: Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction Abstract: Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extracting a good rep... |
Title: A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization Abstract: Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have develo... |
Title: Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization Abstract: Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our ... |
Title: Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations Abstract: Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fideli... |
Title: History-Restricted Online Learning Abstract: We introduce the concept of history-restricted no-regret online learning algorithms. An online learning algorithm $\mathcal{A}$ is $M$-history-restricted if its output at time $t$ can be written as a function of the $M$ previous rewards. This class of online learning ... |
Title: Contributor-Aware Defenses Against Adversarial Backdoor Attacks Abstract: Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform t... |
Title: Additive Higher-Order Factorization Machines Abstract: In the age of big data and interpretable machine learning, approaches need to work at scale and at the same time allow for a clear mathematical understanding of the method's inner workings. While there exist inherently interpretable semi-parametric regressio... |
Title: Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG Abstract: The adoption of deep learning-based healthcare decision support systems such as the detection of irregular cardiac rhythm is hindered by challenges such as lack of access to quality data and the high costs associated with th... |
Title: Introducing Non-Linearity into Quantum Generative Models Abstract: The evolution of an isolated quantum system is linear, and hence quantum algorithms are reversible, including those that utilize quantum circuits as generative machine learning models. However, some of the most successful classical generative mod... |
Title: Optimal Decision Diagrams for Classification Abstract: Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are us... |
Title: SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech Abstract: Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attacke... |
Title: Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline Abstract: We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability stemming from task agnosticism, as well as additional difficulties of continual learni... |
Title: Noise-Aware Statistical Inference with Differentially Private Synthetic Data Abstract: While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing... |
Title: Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit Abstract: In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian disinformation outlets output torrents of misleading and outright false information.... |
Title: Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis Abstract: Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding $\varepsilon$-statio... |
Title: Parameter-Efficient and Student-Friendly Knowledge Distillation Abstract: Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixed (or partially) during training. Recent studies show that ... |
Title: Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors Abstract: Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-... |
Title: ByteComp: Revisiting Gradient Compression in Distributed Training Abstract: Gradient compression (GC) is a promising approach to addressing the communication bottleneck in distributed deep learning (DDL). However, it is challenging to find the optimal compression strategy for applying GC to DDL because of the in... |
Title: Collaborative likelihood-ratio estimation over graphs Abstract: Assuming we have i.i.d observations from two unknown probability density functions (pdfs), $p$ and $p'$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs just by relying on the available data, and without knowing ... |
Title: Visual Perception of Building and Household Vulnerability from Streets Abstract: In developing countries, building codes often are outdated or not enforced. As a result, a large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. Assessing housing quality is ... |
Title: CyCLIP: Cyclic Contrastive Language-Image Pretraining Abstract: Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require jo... |
Title: Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning Abstract: Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decay... |
Title: Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems Abstract: We propose two stochastic gradient algorithms to solve a class of saddle-point problems in a decentralized setting (without a central server). The proposed algorithms are the first to achieve sub-linear/li... |
Title: Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations Abstract: In this paper, we propose MAGICSTYLEGAN and MAGICSTYLEGAN-ADA - both incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to experiment with their capacity of transfer learning into a rather different do... |
Title: Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks Abstract: Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden priv... |
Title: Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty Abstract: Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibili... |
Title: A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks Abstract: Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered eve... |
Title: Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregation Abstract: Deep convolutional neural networks (CNNs) have brought breakthroughs in processing clinical electrocardiograms (ECGs), speaker-independent speech and complex images. However, typical CNNs require a fixe... |
Title: Approximation of Functionals by Neural Network without Curse of Dimensionality Abstract: In this paper, we establish a neural network to approximate functionals, which are maps from infinite dimensional spaces to finite dimensional spaces. The approximation error of the neural network is $O(1/\sqrt{m})$ where $m... |
Title: Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead Abstract: Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient har... |
Title: Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting Abstract: Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribu... |
Title: Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Abstract: Recent progress in deep model-based reinforcement learning allows agents to be significantly more sample efficient by constructing world models of high-dimensional environments from visual observations, which enables agents to l... |
Title: Rethinking the Setting of Semi-supervised Learning on Graphs Abstract: We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of tuning hyp... |
Title: Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition Abstract: As robots are expected to get more involved in people's everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communicatio... |
Title: Deep Learning-based Spatially Explicit Emulation of an Agent-Based Simulator for Pandemic in a City Abstract: Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) an... |
Title: WaveMix-Lite: A Resource-efficient Neural Network for Image Analysis Abstract: Gains in the ability to generalize on image analysis tasks for neural networks have come at the cost of increased number of parameters and layers, dataset sizes, training and test computations, and GPU RAM. We introduce a new architec... |
Title: Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Abstract: Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in... |
Title: Going Deeper into Permutation-Sensitive Graph Neural Networks Abstract: The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite can be satisfied by the invariant operations over node permuta... |
Title: Granular Generalized Variable Precision Rough Sets and Rational Approximations Abstract: Rational approximations are introduced and studied in granular graded sets and generalizations thereof by the first author in recent research papers. The concept of rationality is determined by related ontologies and coheren... |
Title: Fair Labeled Clustering Abstract: Numerous algorithms have been produced for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studied is group fairness, in which proportional group representation is ensured in every cluster. We ex... |
Title: Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images Abstract: The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several condit... |
Title: Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories Abstract: Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound (B&B) algorithm seeks to exactly solve... |
Title: List-Decodable Sparse Mean Estimation Abstract: Robust mean estimation is one of the most important problems in statistics: given a set of samples $\{x_1, \dots, x_n\} \subset \mathbb{R}^d$ where an $\alpha$ fraction are drawn from some distribution $D$ and the rest are adversarially corrupted, it aims to estima... |
Title: Object-wise Masked Autoencoders for Fast Pre-training Abstract: Self-supervised pre-training for images without labels has recently achieved promising performance in image classification. The success of transformer-based methods, ViT and MAE, draws the community's attention to the design of backbone architecture... |
Title: Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers Abstract: Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatical increases in model size without significant incr... |
Title: Teaching Models to Express Their Uncertainty in Words Abstract: We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language -- without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high... |
Title: Survival Analysis on Structured Data using Deep Reinforcement Learning Abstract: Survival analysis is playing a major role in manufacturing sector by analyzing occurrence of any unwanted event based on the input data. Predictive maintenance, which is a part of survival analysis, helps to find any device failure ... |
Title: Feature subset selection for kernel SVM classification via mixed-integer optimization Abstract: We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this ... |
Title: Differentially Private Covariance Revisited Abstract: In this paper, we present three new error bounds, in terms of the Frobenius norm, for covariance estimation under differential privacy: (1) a worst-case bound of $\tilde{O}(d^{1/4}/\sqrt{n})$, which improves the standard Gaussian mechanism $\tilde{O}(d/n)$ fo... |
Title: Multi-agent Databases via Independent Learning Abstract: Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. essential database components, such as the optimizer, sche... |
Title: Automatic Expert Selection for Multi-Scenario and Multi-Task Search Abstract: Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize m... |
Title: Learning from Self-Sampled Correct and Partially-Correct Programs Abstract: Program synthesis aims to generate executable programs that are consistent with the user specification. While there are often multiple programs that satisfy the same user specification, existing neural program synthesis models are often ... |
Title: A Confidence Machine for Sparse High-Order Interaction Model Abstract: In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the confidence of prediction results with fewer theoretical assumpt... |
Title: Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition Abstract: With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs... |
Title: Approximate Conditional Coverage via Neural Model Approximations Abstract: Constructing reliable prediction sets is an obstacle for applications of neural models: Distribution-free conditional coverage is theoretically impossible, and the exchangeability assumption underpinning the coverage guarantees of standar... |
Title: Federated Neural Bandit Abstract: Recent works on neural contextual bandit have achieved compelling performances thanks to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandit involve multiple agents who collaborate witho... |
Title: TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph Abstract: Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, whi... |
Title: Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network Abstract: KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the ... |
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