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Title: Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems Abstract: Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it dir... |
Title: A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game Abstract: Werewolf is a popular party game throughout the world, and research on its significance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abil... |
Title: MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection Abstract: COVID-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reduci... |
Title: A Learning-Based Approach to Approximate Coded Computation Abstract: Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC... |
Title: Deep Learning Methods for Proximal Inference via Maximum Moment Restriction Abstract: The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of... |
Title: Algorithms for Weak Optimal Transport with an Application to Economics Abstract: The theory of weak optimal transport (WOT), introduced by [Gozlan et al., 2017], generalizes the classic Monge-Kantorovich framework by allowing the transport cost between one point and the points it is matched with to be nonlinear.... |
Title: Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles Abstract: Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machi... |
Title: Learning Interface Conditions in Domain Decomposition Solvers Abstract: Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified... |
Title: Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks Abstract: Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique ... |
Title: Concurrent Policy Blending and System Identification for Generalized Assistive Control Abstract: In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create gene... |
Title: Summarization as Indirect Supervision for Relation Extraction Abstract: Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, ... |
Title: Why GANs are overkill for NLP Abstract: This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as popular for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they ... |
Title: HyBNN and FedHyBNN: (Federated) Hybrid Binary Neural Networks Abstract: Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use i... |
Title: A toolbox for idea generation and evaluation: Machine learning, data-driven, and contest-driven approaches to support idea generation Abstract: The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different form... |
Title: Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis Abstract: Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapo... |
Title: Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes Abstract: Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learn... |
Title: MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation Abstract: Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, exist... |
Title: Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates Abstract: We consider optimizing two-layer neural networks in the mean-field regime where the learning dynamics of network weights can be approximated by the evolution in the space of probability measures over the w... |
Title: Recurrent segmentation meets block models in temporal networks Abstract: A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world tempor... |
Title: Automated Scoring for Reading Comprehension via In-context BERT Tuning Abstract: Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such a... |
Title: Service Delay Minimization for Federated Learning over Mobile Devices Abstract: Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike ... |
Title: Transformer with Memory Replay Abstract: Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have very high sample complexity. Memory replay is a mechanism that remembers and reuses past exam... |
Title: Content-Context Factorized Representations for Automated Speech Recognition Abstract: Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information a... |
Title: Incremental Learning with Differentiable Architecture and Forgetting Search Abstract: As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to industry expectations. One technique missing fro... |
Title: Real Time Multi-Object Detection for Helmet Safety Abstract: The National Football League and Amazon Web Services teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help acc... |
Title: Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition Abstract: Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for th... |
Title: A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients Abstract: This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major Nort... |
Title: Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection Abstract: Time series anomaly detection is of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection models have been developed throughout the years based on various assumptions rega... |
Title: Interpolating Compressed Parameter Subspaces Abstract: Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a ... |
Title: Let the Model Decide its Curriculum for Multitask Learning Abstract: Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficulty hierarchy either based on human perception or by exhaustively searching the optimal arrangement. However, human perception of difficulty may... |
Title: Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits Abstract: We prove an instance independent (poly) logarithmic regret for stochastic contextual bandits with linear payoff. Previously, in \cite{chu2011contextual}, a lower bound of $\mathcal{O}(\sqrt{... |
Title: Estimating the frame potential of large-scale quantum circuit sampling using tensor networks up to 50 qubits Abstract: We develop numerical protocols for estimating the frame potential, the 2-norm distance between a given ensemble and the exact Haar randomness, using the \texttt{QTensor} platform. Our tensor-net... |
Title: Minimal Explanations for Neural Network Predictions Abstract: Explaining neural network predictions is known to be a challenging problem. In this paper, we propose a novel approach which can be effectively exploited, either in isolation or in combination with other methods, to enhance the interpretability of neu... |
Title: Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome Abstract: Data augmentation plays a key role in modern machine learning pipelines. While numerous augmentation strategies have been studied in the context of computer vision and natural language processing, less is known for ... |
Title: Sparse Infinite Random Feature Latent Variable Modeling Abstract: We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet process prior. A posteriori, the number of instantiated dimensions in th... |
Title: Can Foundation Models Wrangle Your Data? Abstract: Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these m... |
Title: Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Sets Abstract: The ranking of experiments by expected information gain (EIG) in Bayesian experimental design is sensitive to changes in the model's prior distribution, and the approximation of EIG yielded by sampling will h... |
Title: KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation Abstract: Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation. We propose KERPLE, a framework that generalizes relative p... |
Title: Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder Abstract: This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical ... |
Title: On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach Abstract: Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In... |
Title: CertiFair: A Framework for Certified Global Fairness of Neural Networks Abstract: We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain task are to be treated similarly by the decision m... |
Title: Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning Abstract: Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framewo... |
Title: BayesPCN: A Continually Learnable Predictive Coding Associative Memory Abstract: Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindl... |
Title: Towards Explanation for Unsupervised Graph-Level Representation Learning Abstract: Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's ... |
Title: Conformal Prediction with Temporal Quantile Adjustments Abstract: We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthc... |
Title: Explainable Supervised Domain Adaptation Abstract: Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result in increasing... |
Title: Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions Abstract: Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have demonstrated that a warm start with learned dua... |
Title: Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search Abstract: Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic fun... |
Title: A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning Abstract: The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of variables such that it is importan... |
Title: A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks Abstract: Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the problem of quantifying the ... |
Title: On Tackling Explanation Redundancy in Decision Trees Abstract: Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposal... |
Title: A New Feature Selection Method for LogNNet and its Application for Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values Abstract: Since February-2020, the world has embarked on an intense struggle with the COVID-19 disease, and health systems have come under a tragic pressure as the disease tur... |
Title: FairNorm: Fair and Fast Graph Neural Network Training Abstract: Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art for a number of graph-based learning tasks, which leads to a rise in their employment in various domains. However, it has been shown that GNNs may inherit and even ampli... |
Title: HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece Abstract: We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only 7 head gestures for key selection, word selection, word commitment and wo... |
Title: SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning Abstract: Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data. However, the datasets used for training ML models might b... |
Title: Set-based Meta-Interpolation for Few-Task Meta-Learning Abstract: Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-training tasks are still required for generalization to unseen tasks duri... |
Title: Planning with Diffusion for Flexible Behavior Synthesis Abstract: Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this com... |
Title: RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk Abstract: Failures and anomalies in large-scale software systems are unavoidable incidents. When an issue is detected, operators need to quickly and correctly identify its location to facilitate a swift repair. In this work, we consider the ... |
Title: Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning Abstract: Managing the dynamic regions in the photometric loss formulation has been a main issue for handling the self-supervised depth estimation problem. Most previous methods have alleviated this issue by removing the dynamic regions i... |
Title: The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? Abstract: We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise ... |
Title: Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning Abstract: Machine learning models with explainable predictions are increasingly sought after, especially for real-world, mission-critical applications that require bias detection and risk mit... |
Title: A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection Abstract: Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure ... |
Title: Self-Paced Multi-Agent Reinforcement Learning Abstract: Curriculum reinforcement learning (CRL) aims to speed up learning of a task by changing gradually the difficulty of the task from easy to hard through control of factors such as initial state or environment dynamics. While automating CRL is well studied in ... |
Title: Translating Hanja historical documents to understandable Korean and English Abstract: The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', an... |
Title: Neural Additive Models for Nowcasting Abstract: Deep neural networks (DNNs) are one of the most highlighted methods in machine learning. However, as DNNs are black-box models, they lack explanatory power for their predictions. Recently, neural additive models (NAMs) have been proposed to provide this power while... |
Title: Predicting electrode array impedance after one month from cochlear implantation surgery Abstract: Sensorineural hearing loss can be treated using Cochlear implantation. After this surgery using the electrode array impedance measurements, we can check the stability of the impedance value and the dynamic range. De... |
Title: Towards Consistency in Adversarial Classification Abstract: In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary that a... |
Title: Trend analysis and forecasting air pollution in Rwanda Abstract: Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organizatio... |
Title: Survey on Fair Reinforcement Learning: Theory and Practice Abstract: Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-super... |
Title: Exploring Extreme Parameter Compression for Pre-trained Language Models Abstract: Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g., financ... |
Title: Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks Abstract: Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based BNNs could still yield bad predictive performa... |
Title: Towards biologically plausible Dreaming and Planning Abstract: Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing th... |
Title: ExMo: Explainable AI Model using Inverse Frequency Decision Rules Abstract: In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statement... |
Title: The Sufficiency of Off-policyness: PPO is insufficient according to an Off-policy Measure Abstract: One of the major difficulties of reinforcement learning is learning from {\em off-policy} samples, which are collected by a different policy (behavior policy) from what the algorithm evaluates (the target policy).... |
Title: MaskGAE: Masked Graph Modeling Meets Graph Autoencoders Abstract: We present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. Different from previous graph autoencoders (GAEs), MaskGAE adopts masked graph modeling (MGM) as a principled pretext task: masking a po... |
Title: Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees Abstract: Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields. The validity of existing works heavily relies on solving a series of approximation subproblems with ext... |
Title: A Case of Exponential Convergence Rates for SVM Abstract: Classification is often the first problem described in introductory machine learning classes. Generalization guarantees of classification have historically been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on intractable algorithm... |
Title: Leveraging Relational Information for Learning Weakly Disentangled Representations Abstract: Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of varia... |
Title: The Unreasonable Effectiveness of Deep Evidential Regression Abstract: There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on ... |
Title: Understanding and Mitigating the Uncertainty in Zero-Shot Translation Abstract: Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation (MNMT) system. However, its quality is still not satisfactory due to off-target issues. In this paper, we aim to unde... |
Title: On the Prediction Instability of Graph Neural Networks Abstract: Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems. In this paper, we systematically assess the prediction instability... |
Title: Unintended memorisation of unique features in neural networks Abstract: Neural networks pose a privacy risk due to their propensity to memorise and leak training data. We show that unique features occurring only once in training data are memorised by discriminative multi-layer perceptrons and convolutional neura... |
Title: On Calibration of Ensemble-Based Credal Predictors Abstract: In recent years, several classification methods that intend to quantify epistemic uncertainty have been proposed, either by producing predictions in the form of second-order distributions or sets of probability distributions. In this work, we focus on ... |
Title: A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models Abstract: We study experiment design for the unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design. Unlike the ca... |
Title: Semi-self-supervised Automated ICD Coding Abstract: Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning... |
Title: Kernel Normalized Convolutional Networks Abstract: Existing deep convolutional neural network (CNN) architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm significantly improves model performance, but performs poorly with smaller batch sizes. To address this ... |
Title: Visual Concepts Tokenization Abstract: Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition. Towards this goal, we... |
Title: LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation Abstract: Combinatorial Optimisation problems arise in several application domains and are often formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are not always needed. Several heurist... |
Title: FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels Abstract: Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varyi... |
Title: Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling Abstract: As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and engineering fields... |
Title: Evolving SimGANs to Improve Abnormal Electrocardiogram Classification Abstract: Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world da... |
Title: DDDM: a Brain-Inspired Framework for Robust Classification Abstract: Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnera... |
Title: An Artificial Neural Network Functionalized by Evolution Abstract: The topology of artificial neural networks has a significant effect on their performance. Characterizing efficient topology is a field of promising research in Artificial Intelligence. However, it is not a trivial task and it is mainly experiment... |
Title: Is explainable AI a race against model complexity? Abstract: Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we ex... |
Title: Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration Abstract: Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtained by converting from pre-traine... |
Title: Stochastic resonance neurons in artificial neural networks Abstract: Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the challenge... |
Title: Lifelong Personal Context Recognition Abstract: We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this ... |
Title: The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11 Abstract: Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning relat... |
Title: Neural-Symbolic Models for Logical Queries on Knowledge Graphs Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides good interpretation... |
Title: Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions Abstract: Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (... |
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