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Title: Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction Abstract: The processing and classification of electroencephalographic signals (EEG) are increasingly performed using deep learning frameworks, such as convolutional neural networks (CNNs), to generate ... |
Title: Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads Abstract: Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipedal locomotion work, have not considered locomotio... |
Title: Neural networks embrace learned diversity Abstract: Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform... |
Title: Hardware Trojan Insertion Using Reinforcement Learning Abstract: This paper utilizes Reinforcement Learning (RL) as a means to automate the Hardware Trojan (HT) insertion process to eliminate the inherent human biases that limit the development of robust HT detection methods. An RL agent explores the design spac... |
Title: Data Augmentation for Electrocardiograms Abstract: Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to be trained with large, labelled datasets, which are not available for many predict... |
Title: Application of machine learning for predicting the spread of COVID-19 Abstract: The spread of diseases has been studied for many years, but it receives a particular focus recently due to the outbreak and spread of COVID-19. Studies show that the spread of COVID-19 can be characterized by the Susceptible-Infectio... |
Title: A Siren Song of Open Source Reproducibility Abstract: As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is part of a larger trend of taking action based on assumed ideals, without studying... |
Title: Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method Abstract: We propose an adaptive projection-gradient descent-shrinkage-splitting method (APGDSSM) to integrate penalty based channel pruning into quantization-aware training (QAT). APGDSSM concurren... |
Title: The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization Abstract: Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robu... |
Title: Divergence-aware Federated Self-Supervised Learning Abstract: Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras... |
Title: Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks Abstract: Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal... |
Title: Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing Abstract: In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detectio... |
Title: Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning Abstract: Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server. To minimize the communication cost, introducing sparsi... |
Title: Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks Abstract: We show that deep neural networks that satisfy demographic parity do so through a form of race or gender awareness, and that the more we force a network to be fair, the more accurately we can recover race or gender f... |
Title: Refined Convergence and Topology Learning for Decentralized Optimization with Heterogeneous Data Abstract: One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents. In this paper, we revisit the anal... |
Title: High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models Abstract: We study Langevin dynamics for recovering the planted signal in the spiked matrix model. We provide a "path-wise" characterization of the overlap between the output of the Langevin algorithm and the planted signal. This overlap i... |
Title: Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language Abstract: Spurious correlations are a threat to the trustworthiness of natural language processing systems, motivating research into methods for identifying and eliminating them. However, addressing the problem of spuri... |
Title: IDPG: An Instance-Dependent Prompt Generation Method Abstract: Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained language model and only optimizes a few task-specific prompts. In thi... |
Title: Explain yourself! Effects of Explanations in Human-Robot Interaction Abstract: Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. Howev... |
Title: MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio Abstract: Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in ... |
Title: Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation Abstract: A subgraph is a data structure that can represent various real-world problems. We propose Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn representations of subgraphs. Specifically, give... |
Title: FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks Abstract: Despite their effective use in various fields, many aspects of neural networks are poorly understood. One important way to investigate the characteristics of neural networks is to explore the ... |
Title: Spectral bounds of the $\varepsilon$-entropy of kernel classes Abstract: We develop new upper and lower bounds on the $\varepsilon$-entropy of a unit ball in a reproducing kernel Hilbert space induced by some Mercer kernel $K$. Our bounds are based on the behaviour of eigenvalues of a corresponding integral oper... |
Title: Applying machine learning to predict behavior of bus transport in Warsaw, Poland Abstract: Nowadays, it is possible to collect precise data describing movements of public transport. Specifically, for each bus (or tram) geoposition data can be regularly collected. This includes data for all buses in Warsaw, Polan... |
Title: Attention U-Net as a surrogate model for groundwater prediction Abstract: Numerical simulations of groundwater flow are used to analyze and predict the response of an aquifer system to its change in state by approximating the solution of the fundamental groundwater physical equations. The most used and classical... |
Title: Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis Abstract: The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers fr... |
Title: Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning Abstract: Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ul... |
Title: Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture Abstract: This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks. The model is designed based on the sequence-to-sequence (Seq2Seq) arch... |
Title: Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning Abstract: Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. Generally, if one has multiple tasks on a given dataset, one may finetune different models or use task... |
Title: Self-Labeling Refinement for Robust Representation Learning with Bootstrap Your Own Latent Abstract: In this work, we have worked towards two major goals. Firstly, we have investigated the importance of Batch Normalisation (BN) layers in a non-contrastive representation learning framework called Bootstrap Your O... |
Title: Trajectory Optimization Using Neural Network Gradients of Learned Dynamics Abstract: Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate physics simulators, yet some aspects of the physical world, such as f... |
Title: Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification Abstract: Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representatio... |
Title: Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory to Practice Abstract: In an extant population, how much information do extant individuals provide on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and Turner (2020) studied this question under a number of simplifyi... |
Title: Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering Abstract: Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large neural models, these methods enlist... |
Title: Real order total variation with applications to the loss functions in learning schemes Abstract: Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings. In this paper we propose a loss function consisting of a $r$-order (an)-isotropic total vari... |
Title: Robust Cross-Modal Representation Learning with Progressive Self-Distillation Abstract: The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets, which contributes to its compute and data in... |
Title: Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size Abstract: The sequential hypothesis testing problem is a class of statistical analyses where the sample size is not fixed in advance. Instead, the decision-process takes in new observations sequentially to make real-ti... |
Title: Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention Abstract: Interpretability is an important property for visual models as it helps researchers and users understand the internal mechanism of a complex model. However, generating semantic explanations about the learned repre... |
Title: Towards efficient representation identification in supervised learning Abstract: Humans have a remarkable ability to disentangle complex sensory inputs (e.g., image, text) into simple factors of variation (e.g., shape, color) without much supervision. This ability has inspired many works that attempt to solve th... |
Title: Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching Abstract: The expansion of renewable energy could help realizing the goals of peaking carbon dioxide emissions and carbon neutralization. Some existing grid dispatching methods inte... |
Title: "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks Abstract: Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical appli... |
Title: From graphs to DAGs: a low-complexity model and a scalable algorithm Abstract: Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a differentiable function involving the matrix exponent... |
Title: Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data Abstract: Multimodal pre-training for audio-and-text has recently been proved to be effective and has significantly improved the performance of many downstream speech understanding tasks. However, these state-of-the-art pre-tra... |
Title: Gaussian Processes for Missing Value Imputation Abstract: Missing values are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric models with accurate ... |
Title: Expressiveness and Approximation Properties of Graph Neural Networks Abstract: Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNN architectures... |
Title: FOSTER: Feature Boosting and Compression for Class-Incremental Learning Abstract: The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this... |
Title: Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR Abstract: This study shows an enhancement of IoT that gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any pot... |
Title: Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss Abstract: Deep neural networks suffer from the overconfidence issue in the open world, meaning that classifiers could yield confident, incorrect predictions for out-of-distribution (OOD) samples. Thus, it is an urgent and challenging task ... |
Title: Linear Complexity Randomized Self-attention Mechanism Abstract: Recently, random feature attentions (RFAs) are proposed to approximate the softmax attention in linear time and space complexity by linearizing the exponential kernel. In this paper, we first propose a novel perspective to understand the bias in suc... |
Title: Active Learning with Label Comparisons Abstract: Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annotator. On the other hand, comparing two candidate labels is often much easier. We foc... |
Title: FedCorr: Multi-Stage Federated Learning for Label Noise Correction Abstract: Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have v... |
Title: MA-Dreamer: Coordination and communication through shared imagination Abstract: Multi-agent RL is rendered difficult due to the non-stationary nature of environment perceived by individual agents. Theoretically sound methods using the REINFORCE estimator are impeded by its high-variance, whereas value-function b... |
Title: SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems Abstract: We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performan... |
Title: Rethinking Exponential Averaging of the Fisher Abstract: In optimization for Machine learning (ML), it is typical that curvature-matrix (CM) estimates rely on an exponential average (EA) of local estimates (giving EA-CM algorithms). This approach has little principled justification, but is very often used in pra... |
Title: Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch Abstract: In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the syste... |
Title: Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations Abstract: In practice, optimization models are often prone to unavoidable inaccuracies due to lack of data and dubious assumptions. Traditionally, this placed special emphasis on risk-based and robust formulations, and t... |
Title: Information-theoretic Online Memory Selection for Continual Learning Abstract: A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams. In this work, we investigate the online memory selection problem from an information-theoretic perspect... |
Title: Configuration and Collection Factors for Side-Channel Disassembly Abstract: Myriad uses, methodologies, and channels have been explored for side-channel analysis. However, specific implementation considerations are often unpublished. This paper explores select test configuration and collection parameters, such a... |
Title: Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations Abstract: Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each bandit arm consists of the inner p... |
Title: Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts Abstract: Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured clinical texts. These te... |
Title: Multimodal Machine Learning in Precision Health Abstract: As machine learning and artificial intelligence are more frequently being leveraged to tackle problems in the health sector, there has been increased interest in utilizing them in clinical decision-support. This has historically been the case in single mo... |
Title: Measuring the False Sense of Security Abstract: Recently, several papers have demonstrated how widespread gradient masking is amongst proposed adversarial defenses. Defenses that rely on this phenomenon are considered failed, and can easily be broken. Despite this, there has been little investigation into ways o... |
Title: MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction Abstract: Relation Extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used as a way to tackle the scarci... |
Title: Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations Abstract: Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this c... |
Title: Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning Abstract: The optimal control of non-equilibrium open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general... |
Title: DILEMMA: Self-Supervised Shape and Texture Learning with Transformers Abstract: There is a growing belief that deep neural networks with a shape bias may exhibit better generalization capabilities than models with a texture bias, because shape is a more reliable indicator of the object category. However, we show... |
Title: Edge Continual Learning for Dynamic Digital Twins over Wireless Networks Abstract: Digital twins (DTs) constitute a critical link between the real-world and the metaverse. To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while p... |
Title: SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition Abstract: Learning an egocentric action recognition model from video data is challenging due to distractors (e.g., irrelevant objects) in the background. Further integrating object information into an action model is henc... |
Title: Multi-Label Clinical Time-Series Generation via Conditional GAN Abstract: With wide applications of electronic health records (EHR), deep learning methods have been adopted to analyze EHR data on various tasks such as representation learning, clinical event prediction, and phenotyping. However, due to privacy co... |
Title: DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning Abstract: Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which... |
Title: On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice Abstract: Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice. Different researchers use different kinds of information from the voice sig... |
Title: OutfitTransformer: Learning Outfit Representations for Fashion Recommendation Abstract: Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We present a framework, OutfitTransformer, that us... |
Title: Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning Abstract: Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing their ability t... |
Title: RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification Abstract: Multi-fidelity modelling arises in many situations in computational science and engineering world. It enables accurate inference even when only a small set of accurate data is availab... |
Title: Improved Approximations for Euclidean $k$-means and $k$-median, via Nested Quasi-Independent Sets Abstract: Motivated by data analysis and machine learning applications, we consider the popular high-dimensional Euclidean $k$-median and $k$-means problems. We propose a new primal-dual algorithm, inspired by the c... |
Title: Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression Abstract: Sample-efficient machine learning (SEML) has been widely applied to find optimal latency and power tradeoffs for configurable computer systems. Instead of randomly sampling from the configuration sp... |
Title: Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words Abstract: Word embedding systems such as Word2Vec and GloVe are well-known in deep learning approaches to NLP. This is largely due to their ability to capture semantic relationships between words. In this work we investigated their usefulness ... |
Title: Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach Abstract: Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefo... |
Title: An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model Abstract: An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix. However, it introduces multiple hyper-parameters into the... |
Title: Neural Lagrangian Schr\"odinger Bridge Abstract: Population dynamics is the study of temporal and spatial variation in the size of populations of organisms and is a major part of population ecology. One of the main difficulties in analyzing population dynamics is that we can only obtain observation data with coa... |
Title: Stability and Generalization of Differentially Private Minimax Problems Abstract: In the field of machine learning, many problems can be formulated as the minimax problem, including reinforcement learning, generative adversarial networks, to just name a few. So the minimax problem has attracted a huge amount of ... |
Title: A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges Abstract: Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. Recently, large-scale public datasets and advances in NLP research h... |
Title: Adapting BigScience Multilingual Model to Unseen Languages Abstract: We benchmark different strategies of adding new languages (German and Korean) into the BigScience's pretrained multilingual language model with 1.3 billion parameters that currently supports 13 languages. We investigate the factors that affect ... |
Title: Augmentation-Free Graph Contrastive Learning with Performance Guarantee Abstract: Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL methods highly rely on an augmentation scheme to le... |
Title: Learning to Induce Causal Structure Abstract: The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms operate by generating candidate graphs and then evaluating them using either score-based... |
Title: Lyapunov-Guided Embedding for Hyperparameter Selection in Recurrent Neural Networks Abstract: Recurrent Neural Networks (RNN) are ubiquitous computing systems for sequences and multivariate time series data. While several robust architectures of RNN are known, it is unclear how to relate RNN initialization, arch... |
Title: How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision Abstract: Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when gr... |
Title: Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences Abstract: In recent years, with the continuous progress of science and technology, the number of scientific research achievements is increasing day by day, as the exchange platform and medium of scientific res... |
Title: Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization Abstract: Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a trained classifi... |
Title: JORLDY: a fully customizable open source framework for reinforcement learning Abstract: Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for researchers or students who want to study RL. In... |
Title: Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels Abstract: Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision. To what extent can image-based deep reinforcement learning also benefit from ViT architectur... |
Title: Application of QUBO solver using black-box optimization to structural design for resonance avoidance Abstract: Quadratic unconstrained binary optimization (QUBO) solvers can be applied to design an optimal structure to avoid resonance. QUBO algorithms that work on a classical or quantum device have succeeded in ... |
Title: Assessment of Massively Multilingual Sentiment Classifiers Abstract: Models are increasing in size and complexity in the hunt for SOTA. But what if those 2\% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance ... |
Title: Online Frank-Wolfe with Unknown Delays Abstract: The online Frank-Wolfe (OFW) method has gained much popularity for online convex optimization due to its projection-free property. Previous studies showed that for convex losses, OFW attains $O(T^{3/4})$ regret over general sets and $O(T^{2/3})$ regret over strong... |
Title: Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares Abstract: We consider potentially non-convex optimization problems, for which optimal rates of approximation depend on the dimension of the parameter space and the smoothness of the function to be optimized. In this paper, we... |
Title: External control of a genetic toggle switch via Reinforcement Learning Abstract: We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that would render the algorithm unfeasible for practical ... |
Title: T- Hop: Tensor representation of paths in graph convolutional networks Abstract: We describe a method for encoding path information in graphs into a 3-d tensor. We show a connection between the introduced path representation scheme and powered adjacency matrices. To alleviate the heavy computational demands of w... |
Title: gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach Abstract: In real-world decision optimization, often multiple competing objectives must be taken into account. Following classical reinforcement learning, these objectives have to be combined into a single reward function. In... |
Title: Ischemic Stroke Lesion Segmentation Using Adversarial Learning Abstract: Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment and treatment planning. In this work, we propose a segmenta... |
Title: Team \'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models Abstract: Eye-Tracking data is a very useful source of information to study cognition and especially language comprehension in humans. In this paper, we describe our systems... |
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