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Title: Automated differential equation solver based on the parametric approximation optimization Abstract: The numerical methods for differential equation solution allow obtaining a discrete field that converges towards the solution if the method is applied to the correct problem. Nevertheless, the numerical methods ha... |
Title: Pre-trained Language Models as Re-Annotators Abstract: Annotation noise is widespread in datasets, but manually revising a flawed corpus is time-consuming and error-prone. Hence, given the prior knowledge in Pre-trained Language Models and the expected uniformity across all annotations, we attempt to reduce anno... |
Title: Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy Abstract: Sensors from smart consumer devices have demonstrated high potential to serve as digital biomarkers in the identification of movement disorders in recen... |
Title: Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections Abstract: The increased predictive power of nonlinear models comes at the cost of interpretability of its terms. This trade-off has led to the emergence of eXplainable AI (XAI). XAI attempts to shed light on how models use predict... |
Title: NDGGNET-A Node Independent Gate based Graph Neural Networks Abstract: Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Generally, for a certain ... |
Title: Learning Multitask Gaussian Bayesian Networks Abstract: Major depressive disorder (MDD) requires study of brain functional connectivity alterations for patients, which can be uncovered by resting-state functional magnetic resonance imaging (rs-fMRI) data. We consider the problem of identifying alterations of bra... |
Title: Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models Abstract: The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme st... |
Title: Weak Supervision with Incremental Source Accuracy Estimation Abstract: Motivated by the desire to generate labels for real-time data we develop a method to estimate the dependency structure and accuracy of weak supervision sources incrementally. Our method first estimates the dependency structure associated with... |
Title: Simple Contrastive Graph Clustering Abstract: Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this p... |
Title: Subspace Learning Machine (SLM): Methodology and Performance Abstract: Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discri... |
Title: Unsupervised machine learning for physical concepts Abstract: In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step. ... |
Title: Hierarchical Collaborative Hyper-parameter Tuning Abstract: Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set... |
Title: What is Proxy Discrimination? Abstract: The near universal condemnation of proxy discrimination hides a disagreement over what it is. This work surveys various notions of proxy and proxy discrimination found in prior work and represents them in a common framework. These notions variously turn on statistical depe... |
Title: A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization Abstract: Convex-composite optimization, which minimizes an objective function represented by the sum of a differentiable function and a convex one, is widely used i... |
Title: Evaluation Gaps in Machine Learning Practice Abstract: Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities. In practice, ... |
Title: Spatial-temporal associations representation and application for process monitoring using graph convolution neural network Abstract: Industrial process data reflects the dynamic changes of operation conditions, which mainly refer to the irregular changes in the dynamic associations between different variables in... |
Title: Secure Federated Learning for Neuroimaging Abstract: The amount of biomedical data continues to grow rapidly. However, the ability to collect data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. We present a Secure Federated Learning architecture, Met... |
Title: Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning Abstract: Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor sample exploration, there are still som... |
Title: Salient Object Detection via Bounding-box Supervision Abstract: The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency detection to relieve the labeling effort. Given the bounding box anno... |
Title: Developing cooperative policies for multi-stage reinforcement learning tasks Abstract: Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider the value of using skills that are cooperativ... |
Title: A Unified f-divergence Framework Generalizing VAE and GAN Abstract: Developing deep generative models that flexibly incorporate diverse measures of probability distance is an important area of research. Here we develop an unified mathematical framework of f-divergence generative model, f-GM, that incorporates bo... |
Title: A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning Abstract: While reinforcement learning (RL) provides a framework for learning through trial and error, translating RL algorithms into the real world has remained challenging. A major hurdle to real-world application arises from the dev... |
Title: Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks Abstract: In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at differen... |
Title: Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection Abstract: Under noisy conditions, automatic speech recognition (ASR) can greatly benefit from the addition of visual signals coming from a video of the speaker's face. However, when multiple candidate speakers ... |
Title: Reducing Activation Recomputation in Large Transformer Models Abstract: Training large transformer models is one of the most important computational challenges of modern AI. In this paper, we show how to significantly accelerate training of large transformer models by reducing activation recomputation. Activatio... |
Title: Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation Abstract: Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events. The ability to accurately predict how long accidents will last can ... |
Title: Social Inclusion in Curated Contexts: Insights from Museum Practices Abstract: Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive deci... |
Title: Multifidelity data fusion in convolutional encoder/decoder networks Abstract: We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring significantly less trainable parameters than equivalent f... |
Title: ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild Abstract: We describe an instantiation of a new concept for multimodal multisensor data collection of real life in-the-wild free standing social interactions in the form of a Conference Living Lab (ConfLab). ConfLab c... |
Title: Self-Supervised Anomaly Detection: A Survey and Outlook Abstract: Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth of deep learning models. Recently, the emerg... |
Title: Deep Graph Clustering via Mutual Information Maximization and Mixture Model Abstract: Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. In this paper, we introduce a contrastive learning framework for learning clustering-frien... |
Title: 2-d signature of images and texture classification Abstract: We introduce a proper notion of 2-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a 2-dimensional object such as an image. It thus serves as a low-dimensional fea... |
Title: Efficient Risk-Averse Reinforcement Learning Abstract: In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. A risk measure often focuses on the worst returns out of the agent's experience. As a result, standard methods for risk-averse RL often ignore high-return s... |
Title: Sibylvariant Transformations for Robust Text Classification Abstract: The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label. In this work, we propose the notion of sibylv... |
Title: Human Language Modeling Abstract: Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a human-level exists to connect s... |
Title: Extracting Latent Steering Vectors from Pretrained Language Models Abstract: Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We hypothesize that the information needed t... |
Title: Deep fusion of gray level co-occurrence matrices for lung nodule classification Abstract: Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomograp... |
Title: Privacy Enhancement for Cloud-Based Few-Shot Learning Abstract: Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environments may inflict privacy concerns, e.g., attacks or adversaries that ... |
Title: Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise Abstract: We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design. The algorithm has three components: (1) a subspace... |
Title: Tensor-based Collaborative Filtering With Smooth Ratings Scale Abstract: Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. E... |
Title: Secure and Private Source Coding with Private Key and Decoder Side Information Abstract: The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction. The main additions to... |
Title: On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer Abstract: Autonomously trained agents that are supposed to play video games reasonably well rely either on fast simulation speeds or heavy parallelization across thousands of machines running concurrently. This work e... |
Title: Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory Abstract: Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human unders... |
Title: Fundamental limitations on optimization in variational quantum algorithms Abstract: Exploring quantum applications of near-term quantum devices is a rapidly growing field of quantum information science with both theoretical and practical interests. A leading paradigm to establish such near-term quantum applicati... |
Title: On learning agent-based models from data Abstract: Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, ABMs typically can not estimate agent-specific (or "micro") variables: this is a major limitation which prevents ABMs from harne... |
Title: White-box Testing of NLP models with Mask Neuron Coverage Abstract: Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a number of methods for evaluating how thoroughly the internal behavior ... |
Title: A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks Abstract: In distributed training of deep neural networks or Federated Learning (FL), people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines period... |
Title: Learning to Answer Visual Questions from Web Videos Abstract: Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation an... |
Title: Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN Abstract: In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have n... |
Title: Exploring Viable Algorithmic Options for Learning from Demonstration (LfD): A Parameterized Complexity Approach Abstract: The key to reconciling the polynomial-time intractability of many machine learning tasks in the worst case with the surprising solvability of these tasks by heuristic algorithms in practice s... |
Title: Quality versus speed in energy demand prediction for district heating systems Abstract: In this paper, we consider energy demand prediction in district heating systems. Effective energy demand prediction is essential in combined heat power systems when offering electrical energy in competitive electricity market... |
Title: A Safety Assurable Human-Inspired Perception Architecture Abstract: Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the safety assurance needed in autonomous applications. These include... |
Title: Disentangling A Single MR Modality Abstract: Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks. Current methods learn disentangled representations using either paired multi-modal images with the same underl... |
Title: ALLSH: Active Learning Guided by Local Sensitivity and Hardness Abstract: Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition func... |
Title: Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks Abstract: Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its large array aperture and small wavelengt... |
Title: Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization Abstract: The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical performances in many applications for the minimization of sim... |
Title: Hyperparameter optimization of hybrid quantum neural networks for car classification Abstract: Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require... |
Title: Lifelong Personal Context Recognition Abstract: We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this ... |
Title: Turtle Score -- Similarity Based Developer Analyzer Abstract: In day-to-day life, a highly demanding task for IT companies is to find the right candidates who fit the companies' culture. This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits... |
Title: Symphony Generation with Permutation Invariant Language Model Abstract: In this work, we present a symbolic symphony music generation solution, SymphonyNet, based on a permutation invariant language model. To bridge the gap between text generation and symphony generation task, we propose a novel Multi-track Mult... |
Title: THOR: Threshold-Based Ranking Loss for Ordinal Regression Abstract: In this work, we present a regression-based ordinal regression algorithm for supervised classification of instances into ordinal categories. In contrast to previous methods, in this work the decision boundaries between categories are predefined,... |
Title: Universal Caching Abstract: In the learning literature, the performance of an online policy is commonly measured in terms of the static regret metric, which compares the cumulative loss of an online policy to that of an optimal benchmark in hindsight. In the definition of static regret, the benchmark policy rema... |
Title: Cognitive Visual-learning Environment for PostgreSQL Abstract: PostgreSQL is an object-relational database (ORDBMS) that was introduced into the database community and has been avidly used for a variety of information extraction use cases. It is also known to be an advanced SQL-compliant open source Object RDBMS... |
Title: Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks Abstract: Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex... |
Title: Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs Abstract: The use of machine learning in the self-driving industry has boosted a number of recent advancements. In particular, the usage of large deep learning models in the perception and prediction stack have proved quit... |
Title: Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making Abstract: Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target that is assumed or expected to be unbiased, i.e.... |
Title: Depression Diagnosis and Forecast based on Mobile Phone Sensor Data Abstract: Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easi... |
Title: Matrix and graph representations of vine copula structures Abstract: Vine copulas can efficiently model a large portion of probability distributions. This paper focuses on a more thorough understanding of their structures. We are building on well-known existing constructions to represent vine copulas with graphs... |
Title: Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker Abstract: The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers. Currently, lines of research in neuromorphic engineering are ... |
Title: Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey Abstract: The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different... |
Title: A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism Abstract: Short-term traffic flow prediction is a vital branch of the Intelligent Traffic System (ITS) and plays an important role in traffic management. Graph convolution network (GCN) is widely u... |
Title: Deep learning based Chinese text sentiment mining and stock market correlation research Abstract: We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and ... |
Title: Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks Abstract: This paper introduces a novel neural network -- the flow completion network (FCN) -- to infer the fluid dynamics, including the flow field and the force acting on the body, from the incomp... |
Title: AI training resources for GLAM: a snapshot Abstract: We take a snapshot of current resources available for teaching and learning AI with a focus on the Galleries, Libraries, Archives and Museums (GLAM) community. The review was carried out in 2021 and 2022. The review provides an overview of material we identifi... |
Title: Explainable Data Imputation using Constraints Abstract: Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposi... |
Title: Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy Abstract: The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs) with computational advantages over classical ones. To date, two prototypical QGLMs are quantum cir... |
Title: Weakly-supervised segmentation of referring expressions Abstract: Visual grounding localizes regions (boxes or segments) in the image corresponding to given referring expressions. In this work we address image segmentation from referring expressions, a problem that has so far only been addressed in a fully-super... |
Title: Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures Abstract: With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference pl... |
Title: Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey Abstract: The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are... |
Title: SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures Abstract: Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the edges that connect nodes), a... |
Title: Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards Abstract: Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memory capacity requirements, reach... |
Title: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random Abstract: In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doub... |
Title: A Neural Network Architecture for Program Understanding Inspired by Human Behaviors Abstract: Program understanding is a fundamental task in program language processing. Despite the success, existing works fail to take human behaviors as reference in understanding programs. In this paper, we consider human behav... |
Title: DNS based In-Browser Cryptojacking Detection Abstract: The metadata aspect of Domain Names (DNs) enables us to perform a behavioral study of DNs and detect if a DN is involved in in-browser cryptojacking. Thus, we are motivated to study different temporal and behavioral aspects of DNs involved in cryptojacking. ... |
Title: OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt Abstract: Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as an ef... |
Title: Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs Abstract: Financial markets are highly complex and volatile; thus, learning about such markets for the sake of making predictions is vital to make early alerts about crashes and subsequent recoveries. People ha... |
Title: Improving genetic risk prediction across diverse population by disentangling ancestry representations Abstract: Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European... |
Title: Real-Time Wearable Gait Phase Segmentation For Running And Walking Abstract: Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test ca... |
Title: Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection Abstract: We propose a Model Predictive Control (MPC) method for collision-free navigation that uses amortized variational inference to approximate the distribution of optimal control sequences by training a normalizing flow con... |
Title: Deep Gait Tracking With Inertial Measurement Unit Abstract: This paper presents a convolutional neural network based foot motion tracking with only six-axis Inertial-Measurement-Unit (IMU) sensor data. The presented approach can adapt to various walking conditions by adopting differential and window based input.... |
Title: A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization Abstract: Keyword spotting has gained popularity as a natural way to interact with consumer devices in recent years. However, because of its always-on nature and the variety of speech, it necessitates a low... |
Title: SuMe: A Dataset Towards Summarizing Biomedical Mechanisms Abstract: Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a pr... |
Title: Robust Learning of Parsimonious Deep Neural Networks Abstract: We propose a simultaneous learning and pruning algorithm capable of identifying and eliminating irrelevant structures in a neural network during the early stages of training. Thus, the computational cost of subsequent training iterations, besides tha... |
Title: Crypto Pump and Dump Detection via Deep Learning Techniques Abstract: Despite the fact that cryptocurrencies themselves have experienced an astonishing rate of adoption over the last decade, cryptocurrency fraud detection is a heavily under-researched problem area. Of all fraudulent activity regarding cryptocurr... |
Title: On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis Abstract: The establishment of the link between causality and unsupervised domain adaptation (UDA)/semi-supervised learning (SSL) has led to methodological advances in these learning problems in recent years. Howeve... |
Title: On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning Abstract: The UK anti-fraud charity Fraud Advisory Panel (FAP) in their review of 2016 estimates business costs of fraud at 144 billion, and its individual counterpart at 9.7 billion. Banki... |
Title: KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction Abstract: Predicting future trajectories of road agents is a critical task for autonomous driving. Recent goal-based trajectory prediction methods, such as DenseTNT and PECNet, have shown good performance on prediction ta... |
Title: An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics Abstract: With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensi... |
Title: Risk Aversion In Learning Algorithms and an Application To Recommendation Systems Abstract: Consider a bandit learning environment. We demonstrate that popular learning algorithms such as Upper Confidence Band (UCB) and $\varepsilon$-Greedy exhibit risk aversion: when presented with two arms of the same expectat... |
Title: Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach Abstract: In recent years it has become possible to collect GPS data from drivers and to incorporate this data into automobile insurance pricing for the driver. This data is continuously collected and processed nightly i... |
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