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Title: Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges Abstract: This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggested prom... |
Title: Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation Abstract: Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary seg... |
Title: ESS: Learning Event-based Semantic Segmentation from Still Images Abstract: Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image degradations. Event cameras promise to address these chal... |
Title: Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration Abstract: In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling fo... |
Title: On the Generalization Mystery in Deep Learning Abstract: The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even though they are capable of fitting random datasets of comparable size? Furtherm... |
Title: Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection Abstract: Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of color-coded ... |
Title: Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference Abstract: Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of m... |
Title: Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty Abstract: In stochastic games with incomplete information, the uncertainty is evoked by the lack of knowledge about a player's own and the other players' types, i.e. the utility function and the policy space, and also t... |
Title: SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning Abstract: Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the rewa... |
Title: Lunar Rover Localization Using Craters as Landmarks Abstract: Onboard localization capabilities for planetary rovers to date have used relative navigation, by integrating combinations of wheel odometry, visual odometry, and inertial measurements during each drive to track position relative to the start of each d... |
Title: I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise Abstract: Several real-life applications require crafting concise, quantitative scoring functions (also called rating systems) from measured observations. For example, an effectiveness score needs to be... |
Title: But that's not why: Inference adjustment by interactive prototype deselection Abstract: Despite significant advances in machine learning, decision-making of artificial agents is still not perfect and often requires post-hoc human interventions. If the prediction of a model relies on unreasonable factors it is de... |
Title: FaceMap: Towards Unsupervised Face Clustering via Map Equation Abstract: Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. The main challenge of this task lies in the imperfectness of similarities among image ... |
Title: Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis Abstract: Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome. Capturing brain networks' str... |
Title: SiMCa: Sinkhorn Matrix Factorization with Capacity Constraints Abstract: For a very broad range of problems, recommendation algorithms have been increasingly used over the past decade. In most of these algorithms, the predictions are built upon user-item affinity scores which are obtained from high-dimensional e... |
Title: Seamless lightning nowcasting with recurrent-convolutional deep learning Abstract: A deep learning model is presented to nowcast the occurrence of lightning at a five-minute time resolution 60 minutes into the future. The model is based on a recurrent-convolutional architecture that allows it to recognize and pr... |
Title: Approximate Function Evaluation via Multi-Armed Bandits Abstract: We study the problem of estimating the value of a known smooth function $f$ at an unknown point $\boldsymbol{\mu} \in \mathbb{R}^n$, where each component $\mu_i$ can be sampled via a noisy oracle. Sampling more frequently components of $\boldsymbo... |
Title: Half-Inverse Gradients for Physical Deep Learning Abstract: Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results. Although this combination represents a more complex optimization task than supervised neura... |
Title: Learning Compressed Embeddings for On-Device Inference Abstract: In deep learning, embeddings are widely used to represent categorical entities such as words, apps, and movies. An embedding layer maps each entity to a unique vector, causing the layer's memory requirement to be proportional to the number of entit... |
Title: AI system for fetal ultrasound in low-resource settings Abstract: Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers ... |
Title: Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning Abstract: In this paper, we consider the infinite-horizon reach-avoid zero-sum game problem, where the goal is to find a set in the state space, referred to as the reach-avoid set, such that the system starting at a state therein could b... |
Title: ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Abstract: Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in th... |
Title: A Closer Look at Knowledge Distillation with Features, Logits, and Gradients Abstract: Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. A vast number of methods have been developed for this strategy. While most method designs a mor... |
Title: Privacy-Preserving Reinforcement Learning Beyond Expectation Abstract: Cyber and cyber-physical systems equipped with machine learning algorithms such as autonomous cars share environments with humans. In such a setting, it is important to align system (or agent) behaviors with the preferences of one or more hum... |
Title: Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike Abstract: We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. The original sample is shifted towards a target sample, yielding an adversarial sample, by using the mo... |
Title: Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models Abstract: At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner models are susceptible to ... |
Title: Importance Sampling Placement in Off-Policy Temporal-Difference Methods Abstract: A central challenge to applying many off-policy reinforcement learning algorithms to real world problems is the variance introduced by importance sampling. In off-policy learning, the agent learns about a different policy than the ... |
Title: Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning Abstract: Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The disti... |
Title: Negative Inner-Loop Learning Rates Learn Universal Features Abstract: Model Agnostic Meta-Learning (MAML) consists of two optimization loops: the outer loop learns a meta-initialization of model parameters that is shared across tasks, and the inner loop task-specific adaptation step. A variant of MAML, Meta-SGD,... |
Title: A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models Abstract: The Expectation-Maximization (EM) algorithm is a popular choice for learning latent variable models. Variants of the EM have been initially introduced, using incremental updates to scale to large datasets, and using ... |
Title: Provably Fair Federated Learning via Bounded Group Loss Abstract: In federated learning, fair prediction across various protected groups (e.g., gender, race) is an important constraint for many applications. Unfortunately, prior work studying group fair federated learning lacks formal convergence or fairness gua... |
Title: Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images Abstract: The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for various computer vision tasks has paved the path for numerous opportunities to make them more effective and valuable. Object detection in aerial images i... |
Title: Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation Abstract: We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consists of an encoder and a two-head d... |
Title: Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation Abstract: High-fidelity quantum dynamics emulators can be used to predict the time evolution of complex physical systems. Here, we introduce an efficient training framework for constructing machine learning-based emulators. Our approach i... |
Title: Inferring topological transitions in pattern-forming processes with self-supervised learning Abstract: The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes is critical for understanding and fabricating microstructurally precise novel materia... |
Title: Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate Abstract: We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster conver... |
Title: Thompson Sampling on Asymmetric $\alpha$-Stable Bandits Abstract: In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important. Multi-armed bandit problem can optimize the proposed solutions by changing the reward distribution to realize the... |
Title: Doubly Robust Collaborative Targeted Learning for Debiased Recommendations Abstract: In recommender systems, the collected data always contains various biases and leads to the challenge of accurate predictions. To address selection bias and confounding bias, the doubly robust (DR) method and its variants show su... |
Title: FaiRR: Faithful and Robust Deductive Reasoning over Natural Language Abstract: Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., t... |
Title: Assessing Gender Bias in Predictive Algorithms using eXplainable AI Abstract: Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices ... |
Title: Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily Abstract: Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream tasks. The same aggregation methods and parameter... |
Title: Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features Abstract: Because of the current COVID-19 pandemic with its increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected the developed count... |
Title: Adversarial Defense via Image Denoising with Chaotic Encryption Abstract: In the literature on adversarial examples, white box and black box attacks have received the most attention. The adversary is assumed to have either full (white) or no (black) access to the defender's model. In this work, we focus on the e... |
Title: Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing Abstract: Dynamic demand prediction is a key issue in ride-hailing dispatching. Many methods have been developed to improve the demand prediction accuracy of an increase in demand-responsive, ride-hailing transpor... |
Title: PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs Abstract: Optimization of directed acyclic graph (DAG) structures has many applications, such as neural architecture search (NAS) and probabilistic graphical model learning. Encoding DAGs into real vectors is a dominant component in most neur... |
Title: Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction Abstract: Solving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical exp... |
Title: Practical Recommendations for Replay-based Continual Learning Methods Abstract: Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge. Several approaches have been developed in the literature to tackle the Continual Learning challenge. ... |
Title: Sequence-to-Sequence Knowledge Graph Completion and Question Answering Abstract: Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied to KG link prediction and question answering over i... |
Title: Implicit Parameter-free Online Learning with Truncated Linear Models Abstract: Parameter-free algorithms are online learning algorithms that do not require setting learning rates. They achieve optimal regret with respect to the distance between the initial point and any competitor. Yet, parameter-free algorithms... |
Title: Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm Abstract: Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. A complete list of metrics to evaluate VFL al... |
Title: Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense Abstract: We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K human-written text perturbations in the wild and leverages them for realistic adversarial attack. Unlike existin... |
Title: The Sandbox Environment for Generalizable Agent Research (SEGAR) Abstract: A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress. While there has been notable headway, current benchmarks either do no... |
Title: Deep Learning Generalization, Extrapolation, and Over-parameterization Abstract: We study the generalization of over-parameterized deep networks (for image classification) in relation to the convex hull of their training sets. Despite their great success, generalization of deep networks is considered a mystery. ... |
Title: On Robust Prefix-Tuning for Text Classification Abstract: Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained models fixed and only updates the prefix token parameters for each downstream ta... |
Title: Anomaly Detection in Emails using Machine Learning and Header Information Abstract: Anomalies in emails such as phishing and spam present major security risks such as the loss of privacy, money, and brand reputation to both individuals and organizations. Previous studies on email anomaly detection relied on a si... |
Title: Language-Preserving Reduction Rules for Block-Structured Workflow Nets Abstract: Process models are used by human analysts to model and analyse behaviour, and by machines to verify properties such as soundness, liveness or other reachability properties, and to compare their expressed behaviour with recorded beha... |
Title: Attri-VAE: attribute-based, disentangled and interpretable representations of medical images with variational autoencoders Abstract: Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based ... |
Title: CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration Abstract: Households across the world contain arbitrary objects: from mate gourds and coffee mugs to sitars and guitars. Considering this diversity, robot perception must handle a large variety of semantic objects without addition... |
Title: Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection Abstract: This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the application of linear statistical dimensionality reduction techniques on the s... |
Title: A Study on Robustness to Perturbations for Representations of Environmental Sound Abstract: Audio applications involving environmental sound analysis increasingly use general-purpose audio representations, also known as embeddings, for transfer learning. Recently, Holistic Evaluation of Audio Representations (HE... |
Title: PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication Abstract: Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators suc... |
Title: Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision Abstract: Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual c... |
Title: Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design Abstract: In recent years, quantum computing (QC) has been getting a lot of attention from industry and academia. Especially, among various QC research topics, variational quantum circuit (VQC) enables quantum deep reinforcement le... |
Title: Smoothing Advantage Learning Abstract: Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization. Unfortunately, the method tends to be unstable in the case of function approximation. In this paper, we propose a sim... |
Title: A 3D Molecule Generative Model for Structure-Based Drug Design Abstract: We study a fundamental problem in structure-based drug design -- generating molecules that bind to specific protein binding sites. While we have witnessed the great success of deep generative models in drug design, the existing methods are ... |
Title: Over-parameterization: A Necessary Condition for Models that Extrapolate Abstract: In this work, we study over-parameterization as a necessary condition for having the ability for the models to extrapolate outside the convex hull of training set. We specifically, consider classification models, e.g., image class... |
Title: On the Computation of Necessary and Sufficient Explanations Abstract: The complete reason behind a decision is a Boolean formula that characterizes why the decision was made. This recently introduced notion has a number of applications, which include generating explanations, detecting decision bias and evaluatin... |
Title: CrossBeam: Learning to Search in Bottom-Up Program Synthesis Abstract: Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches sti... |
Title: Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport Abstract: Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. Current studies use existing ... |
Title: Inspection-L: A Self-Supervised GNN-Based Money Laundering Detection System for Bitcoin Abstract: Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars ... |
Title: Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs Abstract: As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new featur... |
Title: ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis Abstract: In recent years, neural network based methods for multi-speaker text-to-speech synthesis (TTS) have made significant progress. However, the current speaker encoder models used in these methods still cannot capture enough speaker information. In this... |
Title: Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey Abstract: In recent years, the prevalent online services generate a sheer volume of user activity data. Service providers collect these data in order to perform client behavior analysis, and offer better and more customized services. Ma... |
Title: MicroRacer: a didactic environment for Deep Reinforcement Learning Abstract: MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with... |
Title: Adversarial Parameter Attack on Deep Neural Networks Abstract: In this paper, a new parameter perturbation attack on DNNs, called adversarial parameter attack, is proposed, in which small perturbations to the parameters of the DNN are made such that the accuracy of the attacked DNN does not decrease much, but it... |
Title: Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions Abstract: The medical datasets are usually faced with the problem of scarcity and data imbalance. Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-knowledge and time-consumin... |
Title: An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs Abstract: Topology Optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires numerical evaluation of objective function and constrai... |
Title: Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions Abstract: In this work, we propose a framework for allowing autonomous robots deployed for extended periods of time in public spaces to adapt their own behaviour online from user interactions. The robot behaviour planning is embedd... |
Title: Reinforcement learning reward function in unmanned aerial vehicle control tasks Abstract: This paper presents a new reward function that can be used for deep reinforcement learning in unmanned aerial vehicle (UAV) control and navigation problems. The reward function is based on the construction and estimation of... |
Title: Stochastic Video Prediction with Structure and Motion Abstract: While stochastic video prediction models enable future prediction under uncertainty, they mostly fail to model the complex dynamics of real-world scenes. For example, they cannot provide reliable predictions for scenes with a moving camera and indep... |
Title: A Learning Convolutional Neural Network Approach for Network Robustness Prediction Abstract: Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain ... |
Title: LEReg: Empower Graph Neural Networks with Local Energy Regularization Abstract: Researches on analyzing graphs with Graph Neural Networks (GNNs) have been receiving more and more attention because of the great expressive power of graphs. GNNs map the adjacency matrix and node features to node representations by ... |
Title: Multi-view Multi-behavior Contrastive Learning in Recommendation Abstract: Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a use... |
Title: Small Batch Sizes Improve Training of Low-Resource Neural MT Abstract: We study the role of an essential hyper-parameter that governs the training of Transformers for neural machine translation in a low-resource setting: the batch size. Using theoretical insights and experimental evidence, we argue against the w... |
Title: Cluster & Tune: Boost Cold Start Performance in Text Classification Abstract: In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is p... |
Title: Neuro-physical dynamic load modeling using differentiable parametric optimization Abstract: In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis. The proposed reduced equivalent is a neuro-phys... |
Title: Variational Quantum Policy Gradients with an Application to Quantum Control Abstract: Quantum Machine Learning models are composed by Variational Quantum Circuits (VQCs) in a very natural way. There are already some empirical results proving that such models provide an advantage in supervised/unsupervised learni... |
Title: Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents Abstract: In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making. Based on this identification, we derive algorithms that exploit these g... |
Title: Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images Abstract: One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This ... |
Title: The Dark Side: Security Concerns in Machine Learning for EDA Abstract: The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies. In recent years, many unprecedented efficient EDA methods have been enabled by machine lear... |
Title: Does DQN really learn? Exploring adversarial training schemes in Pong Abstract: In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained against the built-in Atari opponent. To measure agent... |
Title: Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study Abstract: Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen th... |
Title: Differentiable Reasoning over Long Stories -- Assessing Systematic Generalisation in Neural Models Abstract: Contemporary neural networks have achieved a series of developments and successes in many aspects; however, when exposed to data outside the training distribution, they may fail to predict correct answers... |
Title: Immersive Text Game and Personality Classification Abstract: We designed and built a game called \textit{Immersive Text Game}, which allows the player to choose a story and a character, and interact with other characters in the story in an immersive manner of dialogues. The game is based on several latest models... |
Title: Calibration of Machine Reading Systems at Scale Abstract: In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction. This confidence measure is usually uncalibrated; i.e.\ the system's confidence in the prediction does not ma... |
Title: Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning Abstract: While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low... |
Title: Enriching Unsupervised User Embedding via Medical Concepts Abstract: Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user embedding aims to encode pa... |
Title: Explicit User Manipulation in Reinforcement Learning Based Recommender Systems Abstract: Recommender systems are highly prevalent in the modern world due to their value to both users and platforms and services that employ them. Generally, they can improve the user experience and help to increase satisfaction, bu... |
Title: Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting Abstract: Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary. These properties hinder the development of scalable and efficient solutions for time series forecasting and analysis. To add... |
Title: Fully Convolutional Fractional Scaling Abstract: We introduce a fully convolutional fractional scaling component, FCFS. Fully convolutional networks can be applied to any size input and previously did not support non-integer scaling. Our architecture is simple with an efficient single layer implementation. Examp... |
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