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Title: Learning Behavioral Soft Constraints from Demonstrations Abstract: Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective rules and norm... |
Title: Interpreting Language Models with Contrastive Explanations Abstract: Model interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small. However, when applied to language generation, where the output space often consists o... |
Title: Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection Abstract: We apply convolutional neural networks to identify between malaria infected and non-infected segmented cells from the thin blood smear slide images. We optimize our model to find over 95% accuracy in ... |
Title: Learning Causal Overhypotheses through Exploration in Children and Computational Models Abstract: Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics, which do not consider the underlyin... |
Title: Survey on Large Scale Neural Network Training Abstract: Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU device or can be trained using only a small per-GPU batch size. This survey pro... |
Title: EINNs: Epidemiologically-Informed Neural Networks Abstract: We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI m... |
Title: Transformer Quality in Linear Time Abstract: We revisit the design choices in Transformers, and propose methods to address their weaknesses in handling long sequences. First, we propose a simple layer named gated attention unit, which allows the use of a weaker single-head attention with minimal quality loss. We... |
Title: Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube Abstract: We build a system that enables any human to control a robot hand and arm, simply by demonstrating motions with their own hand. The robot observes the human operator via a single RGB camera and imitates their actions in ... |
Title: SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions Abstract: Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However, the huge combinatorial search s... |
Title: A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs Abstract: While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit co... |
Title: Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses Abstract: Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived em... |
Title: A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network Abstract: Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures th... |
Title: A Clustering Preserving Transformation for k-Means Algorithm Output Abstract: This note introduces a novel clustering preserving transformation of cluster sets obtained from $k$-means algorithm. This transformation may be used to generate new labeled data{}sets from existent ones. It is more flexible that Kleinb... |
Title: Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset Abstract: It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglecte... |
Title: Towards technological adaptation of advanced farming through AI, IoT, and Robotics: A Comprehensive overview Abstract: The population explosion of the 21st century has adversely affected the natural resources with restricted availability of cultivable land, increased average temperatures due to global warming, a... |
Title: A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation Abstract: Network pruning and knowledge distillation are two widely-known model compression methods that efficiently reduce computation cost and model size. A common problem in both pruning and distillation is to determine compre... |
Title: Click-Through Rate Prediction in Online Advertising: A Literature Review Abstract: Predicting the probability that a user will click on a specific advertisement has been a prevalent issue in online advertising, attracting much research attention in the past decades. As a hot research frontier driven by industria... |
Title: A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning Abstract: Evolutionary strategies have recently been shown to achieve competing levels of performance for complex optimization problems in reinforcement learning. In such problems, one ... |
Title: Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data Abstract: Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensive comparison between classical TNs a... |
Title: Accelerating Primal-dual Methods for Regularized Markov Decision Processes Abstract: Entropy regularized Markov decision processes have been widely used in reinforcement learning. This paper is concerned with the primal-dual formulation of the entropy regularized problems. Standard first-order methods suffer fro... |
Title: Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network Abstract: We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to destination (OD) travel demand and ... |
Title: Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees Abstract: Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP). Yet, usually, the privacy... |
Title: Non-Volatile Memory Accelerated Posterior Estimation Abstract: Bayesian inference allows machine learning models to express uncertainty. Current machine learning models use only a single learnable parameter combination when making predictions, and as a result are highly overconfident when their predictions are w... |
Title: Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness Abstract: Adversarial examples, crafted by adding imperceptible perturbations to natural inputs, can easily fool deep neural networks (DNNs). One of the most successful methods for training adversarially robust DNNs is solving a non... |
Title: Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks Abstract: Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protec... |
Title: Privacy Leakage of Adversarial Training Models in Federated Learning Systems Abstract: Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further... |
Title: Imbalanced Classification via Explicit Gradient Learning From Augmented Data Abstract: Learning from imbalanced data is one of the most significant challenges in real-world classification tasks. In such cases, neural networks performance is substantially impaired due to preference towards the majority class. Exi... |
Title: Guidelines and evaluation for clinical explainable AI on medical image analysis Abstract: Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. Applying XAI in clinical settings requires proper ... |
Title: Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation Abstract: Deep learning has been successfully applied to precipitation nowcasting. In this work, we propose a pre-training scheme and a new loss function for improving deep-learning-based nowcasting. First, we adapt U-N... |
Title: GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty Abstract: Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider ... |
Title: Moment Matching Deep Contrastive Latent Variable Models Abstract: In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a background dataset generated from sources of variation irrelevant t... |
Title: CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator Abstract: With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility such as human activity recognition (HAR). Training deep neural network (DNN) model... |
Title: T-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning Abstract: Inspired by the recent success of deep learning in diverse domains, data-driven metamaterials design has emerged as a compelling design paradigm to unlock the potential of multiscale architecture. However, exis... |
Title: Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks Abstract: In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have attempted to model video distribution by rep... |
Title: Deep Iterative Phase Retrieval for Ptychography Abstract: One of the most prominent challenges in the field of diffractive imaging is the phase retrieval (PR) problem: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. This is only possible given the f... |
Title: A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets Abstract: The two-sided markets such as ride-sharing companies often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development of smart phones and internet o... |
Title: Benchmarking missing-values approaches for predictive models on health databases Abstract: BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values: incomplete observations. These large databases are well suited to train machine-learn... |
Title: Unleashing the Power of Transformer for Graphs Abstract: Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs. The computational complexity is unacceptable for large-scale graphs, e.g., knowledge graphs. One solutio... |
Title: Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias Abstract: Backdoor attack is a new AI security risk that has emerged in recent years. Drawing on the previous research of adversarial attack, we argue that the backdoor attack has the potential to tap into the model learn... |
Title: MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned Abstract: Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enfo... |
Title: DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression Abstract: Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center. To maximize data-reduction efficiency, existing po... |
Title: Variational Neural Temporal Point Process Abstract: A temporal point process is a stochastic process that predicts which type of events is likely to happen and when the event will occur given a history of a sequence of events. There are various examples of occurrence dynamics in the daily life, and it is importa... |
Title: Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting Abstract: Time series forecasting plays a key role in a variety of domains. In a lot of real-world scenarios, there exist multiple forecasting entities (e.g. power station in the solar system, stations in the traffic system). A st... |
Title: Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer Abstract: Machine learning, especially deep learning, has greatly advanced molecular studies in the biochemical domain. Most typically, modeling for most molecular tasks have converged to several paradigms. For example, we usually adopt the pre... |
Title: Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process Abstract: This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured... |
Title: Online Caching with Optimistic Learning Abstract: The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem ... |
Title: Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey Abstract: A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and emergency management actions. R... |
Title: Myriad: a real-world testbed to bridge trajectory optimization and deep learning Abstract: We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments. The primary contributions of Myriad are threefold. First, Myriad provides machine learning practitioners access t... |
Title: No-Regret Learning in Partially-Informed Auctions Abstract: Auctions with partially-revealed information about items are broadly employed in real-world applications, but the underlying mechanisms have limited theoretical support. In this work, we study a machine learning formulation of these types of mechanisms,... |
Title: It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation Abstract: We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as... |
Title: Knowledge Base Question Answering by Case-based Reasoning over Subgraphs Abstract: Question answering (QA) over real-world knowledge bases (KBs) is challenging because of the diverse (essentially unbounded) types of reasoning patterns needed. However, we hypothesize in a large KB, reasoning patterns required to ... |
Title: Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces Abstract: Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering data. In this dissertation, we develop techniques ... |
Title: Order-Optimal Error Bounds for Noisy Kernel-Based Bayesian Quadrature Abstract: In this paper, we study the sample complexity of {\em noisy Bayesian quadrature} (BQ), in which we seek to approximate an integral based on noisy black-box queries to the underlying function. We consider functions in a {\em Reproduci... |
Title: Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation Abstract: Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications, these vectors are held by c... |
Title: On the Effectiveness of Adversarial Training against Backdoor Attacks Abstract: DNNs' demand for massive data forces practitioners to collect data from the Internet without careful check due to the unacceptable cost, which brings potential risks of backdoor attacks. A backdoored model always predicts a target cl... |
Title: Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning Abstract: In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks.... |
Title: Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach Abstract: Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising... |
Title: Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Abstract: Data augmentation is commonly applied to improve performance of deep learning by enforcing the knowledge that certain transformations on the input preserve the output. Currently, the correct data augmentation is chos... |
Title: Convergence of online $k$-means Abstract: We prove asymptotic convergence for a general class of $k$-means algorithms performed over streaming data from a distribution: the centers asymptotically converge to the set of stationary points of the $k$-means cost function. To do so, we show that online $k$-means over... |
Title: Equivariant Graph Hierarchy-Based Neural Networks Abstract: Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particu... |
Title: Decentralized Safe Multi-agent Stochastic Optimal Control using Deep FBSDEs and ADMM Abstract: In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances. Safety is mathematically encoded using stochastic control barrier functions ... |
Title: ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges Abstract: This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (... |
Title: Batched Dueling Bandits Abstract: The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential setting where the policy adapts after every comparison. However, in many applications such as search... |
Title: Partial Identification with Noisy Covariates: A Robust Optimization Approach Abstract: Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, or only measurements of their proxies may be av... |
Title: On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization Abstract: Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem. Gaussian process (GP) ... |
Title: Connecting Optimization and Generalization via Gradient Flow Path Length Abstract: Optimization and generalization are two essential aspects of machine learning. In this paper, we propose a framework to connect optimization with generalization by analyzing the generalization error based on the length of optimiza... |
Title: Contrastive-mixup learning for improved speaker verification Abstract: This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weighted combination of random data point and label pairs for deep... |
Title: Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era Abstract: Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors. Yet, with the rapid advanc... |
Title: Transition Matrix Representation of Trees with Transposed Convolutions Abstract: How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making irreversible decisions. However, searching for a tre... |
Title: Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning Abstract: In today's economy, it becomes important for Internet platforms to consider the sequential information design problem to align its long term interest with incentives of the gig service providers. This pape... |
Title: Physics-Informed Graph Learning: A Survey Abstract: An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and complexity of graph data. A lot of research has been evolving around the preserva... |
Title: Submodlib: A Submodular Optimization Library Abstract: Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to fin... |
Title: Graph Lifelong Learning: A Survey Abstract: Graph learning substantially contributes to solving artificial intelligence (AI) tasks in various graph-related domains such as social networks, biological networks, recommender systems, and computer vision. However, despite its unprecedented prevalence, addressing the... |
Title: Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models Abstract: Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings ... |
Title: Quantum Differential Privacy: An Information Theory Perspective Abstract: Differential privacy has been an exceptionally successful concept when it comes to providing provable security guarantees for classical computations. More recently, the concept was generalized to quantum computations. While classical compu... |
Title: EIGNN: Efficient Infinite-Depth Graph Neural Networks Abstract: Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN models may not be able to effectively capture long-range dependencies i... |
Title: Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics Abstract: Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefiniteness which limits its applicability to ker... |
Title: Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain Abstract: Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. MDA is a challenging task due to the severe domain shift, which not only exists between target and source but ... |
Title: Distilled Neural Networks for Efficient Learning to Rank Abstract: Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking ta... |
Title: Improving Systematic Generalization Through Modularity and Augmentation Abstract: Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In this work, we investigate how two well-known modelin... |
Title: CD-ROM: Complementary Deep-Reduced Order Model Abstract: Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems. However, the applicability of the method to non linear high-dimensional dynamical systems such as the Navier... |
Title: Better Private Algorithms for Correlation Clustering Abstract: In machine learning, correlation clustering is an important problem whose goal is to partition the individuals into groups that correlate with their pairwise similarities as much as possible. In this work, we revisit the correlation clustering under ... |
Title: Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution Abstract: Nowadays, thermal infrared satellite remote sensors enable to extract very interesting information at large scale, in particular Land Surface Temperature (LST). However such data are limited in spatial and/or tem... |
Title: Thinking the Fusion Strategy of Multi-reference Face Reenactment Abstract: In recent advances of deep generative models, face reenactment -manipulating and controlling human face, including their head movement-has drawn much attention for its wide range of applicability. Despite its strong expressiveness, it is ... |
Title: A Review of Affective Generation Models Abstract: Affective computing is an emerging interdisciplinary field where computational systems are developed to analyze, recognize, and influence the affective states of a human. It can generally be divided into two subproblems: affective recognition and affective genera... |
Title: Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks Abstract: Rearrangement tasks have been identified as a crucial challenge for intelligent robotic manipulation, but few methods allow for precise construction of unseen structures. We propose a visual foresight model for pick-and-place rea... |
Title: Adaptive Cholesky Gaussian Processes Abstract: We present a method to fit exact Gaussian process models to large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empiri... |
Title: VU-BERT: A Unified framework for Visual Dialog Abstract: The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history. Existing researches tend to employ the modality-specific modules to... |
Title: Explicit Regularization via Regularizer Mirror Descent Abstract: Despite perfectly interpolating the training data, deep neural networks (DNNs) can often generalize fairly well, in part due to the "implicit regularization" induced by the learning algorithm. Nonetheless, various forms of regularization, such as "... |
Title: PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs Abstract: Signed networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While many s... |
Title: Stochastic Causal Programming for Bounding Treatment Effects Abstract: Causal effect estimation is important for numerous tasks in the natural and social sciences. However, identifying effects is impossible from observational data without making strong, often untestable assumptions. We consider algorithms for th... |
Title: Hyper Attention Recurrent Neural Network: Tackling Temporal Covariate Shift in Time Series Analysis Abstract: Analyzing long time series with RNNs often suffers from infeasible training. Segmentation is therefore commonly used in data pre-processing. However, in non-stationary time series, there exists often dis... |
Title: Robust and Provable Guarantees for Sparse Random Embeddings Abstract: In this work, we improve upon the guarantees for sparse random embeddings, as they were recently provided and analyzed by Freksen at al. (NIPS'18) and Jagadeesan (NIPS'19). Specifically, we show that (a) our bounds are explicit as opposed to t... |
Title: Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness Abstract: In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects. We refer to this as introduced unfairness, and invest... |
Title: Increasing Depth of Neural Networks for Life-long Learning Abstract: Increasing neural network depth is a well-known method for improving neural network performance. Modern deep architectures contain multiple mechanisms that allow hundreds or even thousands of layers to train. This work is trying to answer if ex... |
Title: UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography Abstract: Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accu... |
Title: Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals Abstract: Massive efforts are made to reduce biases in both data and algorithms in order to render AI applications fair. These efforts are propelled by various high-profile cases where biased algorithmic de... |
Title: NU HLT at CMCL 2022 Shared Task: Multilingual and Crosslingual Prediction of Human Reading Behavior in Universal Language Space Abstract: In this paper, we present a unified model that works for both multilingual and crosslingual prediction of reading times of words in various languages. The secret behind the su... |
Title: Choquet-Based Fuzzy Rough Sets Abstract: Fuzzy rough set theory can be used as a tool for dealing with inconsistent data when there is a gradual notion of indiscernibility between objects. It does this by providing lower and upper approximations of concepts. In classical fuzzy rough sets, the lower and upper app... |
Title: Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation Abstract: Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biolog... |
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