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Title: Distillation Decision Tree Abstract: Black-box machine learning models are criticized as lacking interpretability, although they tend to have good prediction accuracy. Knowledge Distillation (KD) is an emerging tool to interpret the black-box model by distilling its knowledge into a transparent model. With well-... |
Title: DiSparse: Disentangled Sparsification for Multitask Model Compression Abstract: Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, w... |
Title: Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry Abstract: The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algor... |
Title: Overcoming the Spectral Bias of Neural Value Approximation Abstract: Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are uni... |
Title: Neural Prompt Search Abstract: The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-efficient tuning methods, such as learning adapter layers or visual prompt tokens, which allow a tiny por... |
Title: Extending Momentum Contrast with Cross Similarity Consistency Regularization Abstract: Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay... |
Title: Can Backdoor Attacks Survive Time-Varying Models? Abstract: Backdoors are powerful attacks against deep neural networks (DNNs). By poisoning training data, attackers can inject hidden rules (backdoors) into DNNs, which only activate on inputs containing attack-specific triggers. While existing work has studied b... |
Title: ReCo: A Dataset for Residential Community Layout Planning Abstract: Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of r... |
Title: POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples Abstract: In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit the easily available out-of-distribution sam... |
Title: RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation Abstract: Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions. To achieve fast and accurate visual assis... |
Title: Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference Abstract: By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer... |
Title: Unsupervised Deep Discriminant Analysis Based Clustering Abstract: This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and maximize the inter-cluster discrepancy in an unsupervised manner. The... |
Title: Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs Abstract: The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter servers with powerful CPUs and GPUs, modern smartphones cons... |
Title: NNTrainer: Light-Weight On-Device Training Framework Abstract: Modern consumer electronic devices have adopted deep learning-based intelligence services for their key features. Vendors have recently started to execute intelligence services on devices to preserve personal data in devices, reduce network and cloud... |
Title: AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing Abstract: $\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness from a single optical... |
Title: A Resilient Distributed Boosting Algorithm Abstract: Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize. We present a distributed boosting algorithm which is resilient to a limited amount of noise. ... |
Title: On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Abstract: Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the simple FedAvg algorithm converges very well. This... |
Title: COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning Abstract: Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA on various downstream tasks. The graph augmentation step is a vital but scarcely studied step of GCL. In this paper, we show that the... |
Title: STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer Abstract: Modeling neural population dynamics underlying noisy single-trial spiking activities is essential for relating neural observation and behavior. A recent non-recurrent method - Neural Data Transformers (NDT) - has shown great s... |
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: Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance Abstract: Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unloc... |
Title: AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging Abstract: This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database w... |
Title: Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination Abstract: Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration, is capable of superb sample efficiency, b... |
Title: A Novel Partitioned Approach for Reduced Order Model -- Finite Element Model (ROM-FEM) and ROM-ROM Coupling Abstract: Partitioned methods allow one to build a simulation capability for coupled problems by reusing existing single-component codes. In so doing, partitioned methods can shorten code development and v... |
Title: I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs Abstract: Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrast... |
Title: A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models Abstract: Responsible use of machine learning requires that models be audited for undesirable properties. However, how to do principled auditing in a general setting has remained ill-understood. In this paper, we propose a formal le... |
Title: Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning Abstract: We present a full implementation and simulation of a novel quantum reinforcement learning (RL) method and mathematically prove a quantum advantage. Our approach shows in detail ho... |
Title: Mobility Improves the Convergence of Asynchronous Federated Learning Abstract: This paper studies asynchronous Federated Learning (FL) subject to clients' individual arbitrary communication patterns with the parameter server. We propose FedMobile, a new asynchronous FL algorithm that exploits the mobility attrib... |
Title: Strong Memory Lower Bounds for Learning Natural Models Abstract: We give lower bounds on the amount of memory required by one-pass streaming algorithms for solving several natural learning problems. In a setting where examples lie in $\{0,1\}^d$ and the optimal classifier can be encoded using $\kappa$ bits, we s... |
Title: Mildly Conservative Q-Learning for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necess... |
Title: HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration Abstract: HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on ... |
Title: An Empirical Study on Disentanglement of Negative-free Contrastive Learning Abstract: Negative-free contrastive learning has attracted a lot of attention with simplicity and impressive performance for large-scale pretraining. But its disentanglement property remains unexplored. In this paper, we take different n... |
Title: Data-Efficient Double-Win Lottery Tickets from Robust Pre-training Abstract: Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate such enormous pre-trained models can be replaced by extreme... |
Title: Neural Bregman Divergences for Distance Learning Abstract: Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algor... |
Title: Joint Entropy Search For Maximally-Informed Bayesian Optimization Abstract: Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy... |
Title: What should AI see? Using the Public's Opinion to Determine the Perception of an AI Abstract: Deep neural networks (DNN) have made impressive progress in the interpretation of image data, so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. Fr... |
Title: Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models Abstract: We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator which is known to be effective agai... |
Title: Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Abstract: Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, to date, offline reinf... |
Title: ReFace: Real-time Adversarial Attacks on Face Recognition Systems Abstract: Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an input-dependent optimization problem using gradient descent w... |
Title: On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks Abstract: We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the... |
Title: Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation Abstract: Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap... |
Title: Comprehensive Fair Meta-learned Recommender System Abstract: In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation sce... |
Title: Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream Abstract: Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based... |
Title: Stable and memory-efficient image recovery using monotone operator learning (MOL) Abstract: We introduce a monotone deep equilibrium learning framework for large-scale inverse problems in imaging. The proposed algorithm relies on forward-backward splitting, where each iteration consists of a gradient descent inv... |
Title: Learning to Efficiently Propagate for Reasoning on Knowledge Graphs Abstract: Path-based methods are more appealing solutions than embedding methods for knowledge graph reasoning, due to their interpretability and generalization ability to unseen graphs. However, path-based methods usually suffer from the proble... |
Title: Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey Abstract: Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the res... |
Title: Learning Attention-based Representations from Multiple Patterns for Relation Prediction in Knowledge Graphs Abstract: Knowledge bases, and their representations in the form of knowledge graphs (KGs), are naturally incomplete. Since scientific and industrial applications have extensively adopted them, there is a ... |
Title: Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics Abstract: The rise of blockchain and distributed ledger technologies (DLTs) in the financial sector has generated a socio-economic shift that triggered legal concerns and regulatory initiatives. While the ... |
Title: Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression Abstract: The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN has succeeded in many m... |
Title: Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022 Abstract: We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our... |
Title: Syntactic Inductive Biases for Deep Learning Methods Abstract: In this thesis, we try to build a connection between the two schools by introducing syntactic inductive biases for deep learning models. We propose two families of inductive biases, one for constituency structure and another one for dependency struct... |
Title: Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems Abstract: Data assimilation (DA) is a key component of many forecasting models in science and engineering. DA allows one to estimate better initial conditions using an imperfect dynamical model of the system ... |
Title: Empirical Bayes approach to Truth Discovery problems Abstract: When aggregating information from conflicting sources, one's goal is to find the truth. Most real-value \emph{truth discovery} (TD) algorithms try to achieve this goal by estimating the competence of each source and then aggregating the conflicting i... |
Title: The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon Abstract: The grokking phenomenon as reported by Power et al. ( arXiv:2201.02177 ) refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization. In this p... |
Title: Training Neural Networks using SAT solvers Abstract: We propose an algorithm to explore the global optimization method, using SAT solvers, for training a neural net. Deep Neural Networks have achieved great feats in tasks like-image recognition, speech recognition, etc. Much of their success can be attributed to... |
Title: Communication Efficient Distributed Learning for Kernelized Contextual Bandits Abstract: We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distributed bandit learning, existing solutions are ... |
Title: In Defense of Core-set: A Density-aware Core-set Selection for Active Learning Abstract: Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, considering the diversity of the selected samples i... |
Title: Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering Abstract: To avoid the curse of dimensionality, a common approach to clustering high-dimensional data is to first project the data into a space of reduced dimension, and then cluster the projected data. Although effective, th... |
Title: Neural Laplace: Learning diverse classes of differential equations in the Laplace domain Abstract: Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities, whic... |
Title: Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition Abstract: Human activity recognition (HAR) through wearable devices has received much interest due to its numerous applications in fitness tracking, wellness screening, and... |
Title: Mixed integer linear optimization formulations for learning optimal binary classification trees Abstract: Decision trees are powerful tools for classification and regression that attract many researchers working in the burgeoning area of machine learning. One advantage of decision trees over other methods is the... |
Title: Conformal Prediction Intervals for Markov Decision Process Trajectories Abstract: Before delegating a task to an autonomous system, a human operator may want a guarantee about the behavior of the system. This paper extends previous work on conformal prediction for functional data and conformalized quantile regre... |
Title: Symbolic image detection using scene and knowledge graphs Abstract: Sometimes the meaning conveyed by images goes beyond the list of objects they contain; instead, images may express a powerful message to affect the viewers' minds. Inferring this message requires reasoning about the relationships between the obj... |
Title: Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction Abstract: Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still has limitations such as potential data pr... |
Title: Multi-fidelity Hierarchical Neural Processes Abstract: Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-fidelity surrogate modeling reduces the computational cost by fusing different ... |
Title: Imitation Learning via Differentiable Physics Abstract: Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this w... |
Title: Efficient Per-Shot Convex Hull Prediction By Recurrent Learning Abstract: Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints. The traditional method of content dependent bitrate ladder selection requi... |
Title: $\mathsf{G^2Retro}$: Two-Step Graph Generative Models for Retrosynthesis Prediction Abstract: Retrosynthesis is a procedure where a molecule is transformed into potential reactants and thus the synthesis routes are identified. We propose a novel generative framework, denoted as $\mathsf{G^2Retro}$, for one-step ... |
Title: Deep Leakage from Model in Federated Learning Abstract: Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn increasing attentions from both academia and industry. In this context, federat... |
Title: Adversarial Counterfactual Environment Model Learning Abstract: A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, recommender systems, and patients' treatment selection. We can take unli... |
Title: Explaining Neural Networks without Access to Training Data Abstract: We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues. Recently, $\mathcal{I}$-Nets have been proposed as a sample-free approach to pos... |
Title: Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns Abstract: We study the problem of consistently recovering the sparsity pattern of a regression parameter vector from correlated observations governed by deterministic missing data patterns using Lasso. We consider the case in whic... |
Title: Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering Abstract: To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image features, which is ... |
Title: Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes Abstract: Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outcome metric. However, in... |
Title: NAGphormer: Neighborhood Aggregation Graph Transformer for Node Classification in Large Graphs Abstract: Graph Transformers have demonstrated superiority on various graph learning tasks in recent years. However, the complexity of existing Graph Transformers scales quadratically with the number of nodes, making i... |
Title: Fisher SAM: Information Geometry and Sharpness Aware Minimisation Abstract: Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness. SAM essentially modifies the loss function by reporting the maximum loss value within the smal... |
Title: Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality Abstract: Goal-oriented Reinforcement Learning, where the agent needs to reach the goal state while simultaneously minimizing the cost, has received significant attention in real-world applications. Its theoretical formulation, stochas... |
Title: A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model Abstract: The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that foc... |
Title: MAREO: Memory- and Attention- based visual REasOning Abstract: Humans continue to vastly outperform modern AI systems in their ability to parse and understand complex visual scenes flexibly. Attention and memory are two systems known to play a critical role in our ability to selectively maintain and manipulate b... |
Title: Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022) Abstract: Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features us... |
Title: Deep Multi-view Semi-supervised Clustering with Sample Pairwise Constraints Abstract: Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the signi... |
Title: Evolutionary Echo State Network: evolving reservoirs in the Fourier space Abstract: The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have recently received significant attention due to t... |
Title: Merak: A Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models Abstract: Foundation models are becoming the dominant deep learning technologies. Pretraining a foundation model is always time-consumed due to the large scale of both the model parameter and training ... |
Title: Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G Abstract: Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mec... |
Title: Refining neural network predictions using background knowledge Abstract: Recent work has showed we can use logical background knowledge in learning system to compensate for a lack of labeled training data. Many such methods work by creating a loss function that encodes this knowledge. However, often the logic is... |
Title: Convolutional Layers Are Not Translation Equivariant Abstract: The purpose of this paper is to correct a misconception about convolutional neural networks (CNNs). CNNs are made up of convolutional layers which are shift equivariant due to weight sharing. However, contrary to popular belief, convolutional layers ... |
Title: Zero-Shot Audio Classification using Image Embeddings Abstract: Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typically requires manual labeling which is expensive and time-consuming. Z... |
Title: The Generalized Eigenvalue Problem as a Nash Equilibrium Abstract: The generalized eigenvalue problem (GEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such as canonical correlation analysis, independent components analysis, partial l... |
Title: Weighted Ensembles for Active Learning with Adaptivity Abstract: Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects... |
Title: Spatial Cross-Attention Improves Self-Supervised Visual Representation Learning Abstract: Unsupervised representation learning methods like SwAV are proved to be effective in learning visual semantics of a target dataset. The main idea behind these methods is that different views of a same image represent the sa... |
Title: Explanation as Question Answering based on a Task Model of the Agent's Design Abstract: We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through participatory design by focus groups. We cap... |
Title: Scalable Deep Gaussian Markov Random Fields for General Graphs Abstract: Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models ... |
Title: Improved Approximation for Fair Correlation Clustering Abstract: Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study Fair Correlation Clustering where the data points may belong to different protected group... |
Title: Temporal Inductive Logic Reasoning Abstract: Inductive logic reasoning is one of the fundamental tasks on graphs, which seeks to generalize patterns from the data. This task has been studied extensively for traditional graph datasets such as knowledge graphs (KGs), with representative techniques such as inductiv... |
Title: Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI Abstract: Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging. Even though machine ... |
Title: Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms Abstract: The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website applic... |
Title: On Neural Architecture Inductive Biases for Relational Tasks Abstract: Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generalization. This is especially true in the case of tasks involving abstract relations like recognizing rule... |
Title: We Cannot Guarantee Safety: The Undecidability of Graph Neural Network Verification Abstract: Graph Neural Networks (GNN) are commonly used for two tasks: (whole) graph classification and node classification. We formally introduce generically formulated decision problems for both tasks, corresponding to the foll... |
Title: Diffeomorphic Counterfactuals with Generative Models Abstract: Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transfo... |
Title: Tensor Train for Global Optimization Problems in Robotics Abstract: The convergence of many numerical optimization techniques is highly sensitive to the initial guess provided to the solver. We propose an approach based on tensor methods to initialize the existing optimization solvers close to global optima. The... |
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