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Title: Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach Abstract: Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. To obtain the robust and sparse estimators, a natural idea is to adopt ... |
Title: Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer Abstract: Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between t... |
Title: Support Vectors and Gradient Dynamics of Single-Neuron ReLU Networks Abstract: Understanding implicit bias of gradient descent for generalization capability of ReLU networks has been an important research topic in machine learning research. Unfortunately, even for a single ReLU neuron trained with the square los... |
Title: Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs Abstract: In electricity markets, retailers or brokers want to maximize profits by allocating tariff profiles to end consumers. One of the objectives of such demand response management is to incentivize the consumers to adjust their ... |
Title: What Does it Mean for a Language Model to Preserve Privacy? Abstract: Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in... |
Title: From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach Abstract: Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only conside... |
Title: Cyclical Curriculum Learning Abstract: Artificial neural networks (ANN) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum Learning (CL) refers to the process of ANN training in which examples are used in a meaningful order. When using CL, trainin... |
Title: A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification Abstract: Traffic classification, i.e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This ta... |
Title: Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data Abstract: Deep learning techniques have recently shown promise in the field of anomaly detection, providing a flexible and effective method of modelling systems in comparison to traditional statistical modelling an... |
Title: Controlling Confusion via Generalisation Bounds Abstract: We establish new generalisation bounds for multiclass classification by abstracting to a more general setting of discretised error types. Extending the PAC-Bayes theory, we are hence able to provide fine-grained bounds on performance for multiclass classi... |
Title: Shuffle Private Linear Contextual Bandits Abstract: Differential privacy (DP) has been recently introduced to linear contextual bandits to formally address the privacy concerns in its associated personalized services to participating users (e.g., recommendations). Prior work largely focus on two trust models of ... |
Title: On change of measure inequalities for $f$-divergences Abstract: We propose new change of measure inequalities based on $f$-divergences (of which the Kullback-Leibler divergence is a particular case). Our strategy relies on combining the Legendre transform of $f$-divergences and the Young-Fenchel inequality. By e... |
Title: Similarity learning for wells based on logging data Abstract: One of the first steps during the investigation of geological objects is the interwell correlation. It provides information on the structure of the objects under study, as it comprises the framework for constructing geological models and assessing hyd... |
Title: Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks Abstract: The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create addition... |
Title: The Shapley Value in Machine Learning Abstract: Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game theory and axiomatic properties of the Shapley... |
Title: Online Decision Transformer Abstract: Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involv... |
Title: Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities Abstract: Radio Frequency (RF) breakdowns are one of the most prevalent limiting factors in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface resu... |
Title: Inference and FDR Control for Simulated Ising Models in High-dimension Abstract: This paper studies the consistency and statistical inference of simulated Ising models in the high dimensional background. Our estimators are based on the Markov chain Monte Carlo maximum likelihood estimation (MCMC-MLE) method pena... |
Title: CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning Abstract: Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods... |
Title: Measuring dissimilarity with diffeomorphism invariance Abstract: Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of data spaces, which leverages the data's internal structure to be in... |
Title: Long-Time Convergence and Propagation of Chaos for Nonlinear MCMC Abstract: In this paper, we study the long-time convergence and uniform strong propagation of chaos for a class of nonlinear Markov chains for Markov chain Monte Carlo (MCMC). Our technique is quite simple, making use of recent contraction estimat... |
Title: A Wasserstein GAN for Joint Learning of Inpainting and its Spatial Optimisation Abstract: Classic image inpainting is a restoration method that reconstructs missing image parts. However, a carefully selected mask of known pixels that yield a high quality inpainting can also act as a sparse image representation. ... |
Title: Scale-free Unconstrained Online Learning for Curved Losses Abstract: A sequence of works in unconstrained online convex optimisation have investigated the possibility of adapting simultaneously to the norm $U$ of the comparator and the maximum norm $G$ of the gradients. In full generality, matching upper and low... |
Title: Efficient Kernel UCB for Contextual Bandits Abstract: In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an effici... |
Title: Bernstein Flows for Flexible Posteriors in Variational Bayes Abstract: Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization. In contrast to MCMC, VI scales to many observations. In the case of complex posteriors, however, state-of-the-art VI approaches often yi... |
Title: InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification Abstract: We present a novel approach to evaluate the performance of interpretability methods for time series classification, and propose a new strategy to assess the similarity between do... |
Title: Predictive modeling of microbiological seawater quality classification in karst region using cascade model Abstract: In this paper, an in-depth analysis of Escherichia coli seawater measurements during the bathing season in the city of Rijeka, Croatia was conducted. Submerged sources of groundwater were observed... |
Title: Deep artificial neural network for prediction of atrial fibrillation through the analysis of 12-leads standard ECG Abstract: Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often asymptomatic, represents an important risk factor for stroke, therefore being able to predict AF at the electroc... |
Title: Rethinking Graph Convolutional Networks in Knowledge Graph Completion Abstract: Graph convolutional networks (GCNs) -- which are effective in modeling graph structures -- have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC models first use GCNs to generate expressive entity represen... |
Title: SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification Abstract: Convolutional neural networks (CNNs) have achieved great success in skin lesion classification. A balanced dataset is required to train a good model. However, due to the appearance of different skin lesions in practice,... |
Title: Graphon-aided Joint Estimation of Multiple Graphs Abstract: We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is a nonparamet... |
Title: Towards Adversarially Robust Deepfake Detection: An Ensemble Approach Abstract: Detecting deepfakes is an important problem, but recent work has shown that DNN-based deepfake detectors are brittle against adversarial deepfakes, in which an adversary adds imperceptible perturbations to a deepfake to evade detecti... |
Title: Continual Learning with Invertible Generative Models Abstract: Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehears... |
Title: Positive-Unlabeled Domain Adaptation Abstract: Domain Adaptation methodologies have shown to effectively generalize from a labeled source domain to a label scarce target domain. Previous research has either focused on unlabeled domain adaptation without any target supervision or semi-supervised domain adaptation... |
Title: Machine Learning for Stock Prediction Based on Fundamental Analysis Abstract: Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods co... |
Title: Molecule Generation from Input-Attributions over Graph Convolutional Networks Abstract: It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the... |
Title: Semi-Supervised GCN for learning Molecular Structure-Activity Relationships Abstract: Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose t... |
Title: Cross Domain Few-Shot Learning via Meta Adversarial Training Abstract: Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, this assumption is not al... |
Title: Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature Abstract: This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperat... |
Title: Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems Abstract: In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a position to design systems t... |
Title: Audio Defect Detection in Music with Deep Networks Abstract: With increasing amounts of music being digitally transferred from production to distribution, automatic means of determining media quality are needed. Protection mechanisms in digital audio processing tools have not eliminated the need of production en... |
Title: Recovering Stochastic Dynamics via Gaussian Schr\"odinger Bridges Abstract: We propose a new framework to reconstruct a stochastic process $\left\{\mathbb{P}_{t}: t \in[0, T]\right\}$ using only samples from its marginal distributions, observed at start and end times $0$ and $T$. This reconstruction is useful to... |
Title: On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems Abstract: Speaker verification systems have been widely used in smart phones and Internet of things devices to identify a legitimate user. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effecti... |
Title: SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography Abstract: Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensi... |
Title: Improving Generalization via Uncertainty Driven Perturbations Abstract: Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias - the tendency of gradient-based algorithms to learn simple models - which include the model's high sensitivity to small input perturbations, as well as sub-optimal m... |
Title: Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning Abstract: The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables. These latent variables include unresolved small scales... |
Title: A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning Abstract: Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signa... |
Title: Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models Abstract: Attacks on deep learning models are often difficult to identify and therefore are difficult to protect against. This problem is exacerbated by the use of public datasets that typically are not manually inspected bef... |
Title: Learning via nonlinear conjugate gradients and depth-varying neural ODEs Abstract: The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding the weights of a residual network with time continu... |
Title: A PDE-Based Analysis of the Symmetric Two-Armed Bernoulli Bandit Abstract: This work addresses a version of the two-armed Bernoulli bandit problem where the sum of the means of the arms is one (the symmetric two-armed Bernoulli bandit). In a regime where the gap between these means goes to zero and the number of... |
Title: Inference of Multiscale Gaussian Graphical Model Abstract: Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields such as genomics, ecology, psychometry. In a high-dimensional setting, when the number of variables exceeds the number of observations by several orders of m... |
Title: A Modern Self-Referential Weight Matrix That Learns to Modify Itself Abstract: The weight matrix (WM) of a neural network (NN) is its program. The programs of many traditional NNs are learned through gradient descent in some error function, then remain fixed. The WM of a self-referential NN, however, can keep ra... |
Title: The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance Abstract: We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for minimizing non-convex, smo... |
Title: Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system Abstract: Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of prior knowledge of potenti... |
Title: Distributionally Robust Data Join Abstract: Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset. What is the most principled way to use these datasets together to construct a predictor? The answer should depend u... |
Title: The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention Abstract: Linear layers in neural networks (NNs) trained by gradient descent can be expressed as a key-value memory system which stores all training datapoints and the initial weights, an... |
Title: Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics Abstract: The theory of reinforcement learning currently suffers from a mismatch between its empirical performance and the theoretical characterization of its performance, with consequences for, e.g., the understanding o... |
Title: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing Abstract: There is a growing interest in the decentralized optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is... |
Title: Investigating Power laws in Deep Representation Learning Abstract: Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning. Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets w... |
Title: Distributed saddle point problems for strongly concave-convex functions Abstract: In this paper, we propose GT-GDA, a distributed optimization method to solve saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}} \{F(\mathbf{x},\mathbf{y}) :=G(\mathbf{x}) + \langle \mathbf{y}, \overline{P} \mat... |
Title: PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition? Abstract: This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalitie... |
Title: End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking Abstract: Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capability is logical extrapo... |
Title: Automated Architecture Search for Brain-inspired Hyperdimensional Computing Abstract: This paper represents the first effort to explore an automated architecture search for hyperdimensional computing (HDC), a type of brain-inspired neural network. Currently, HDC design is largely carried out in an application-sp... |
Title: Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality Abstract: Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models typically require hundreds of forwar... |
Title: SafePicking: Learning Safe Object Extraction via Object-Level Mapping Abstract: Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition and reconstruction can identify the boundary of objec... |
Title: Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing Abstract: Internet of things (IoT) devices, such as smart meters, smart speakers and activity monitors, have become highly popular thanks to the services they offer. However, in addition to their many benefits, they raise privacy conc... |
Title: Predicting Out-of-Distribution Error with the Projection Norm Abstract: We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model predictions to pseudo-label test samples and then trains a n... |
Title: Applications of Machine Learning to Lattice Quantum Field Theory Abstract: There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process... |
Title: Abstraction for Deep Reinforcement Learning Abstract: We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the n... |
Title: Fast Model-based Policy Search for Universal Policy Networks Abstract: Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal policy networks partially address this issue by enabling the stora... |
Title: Uncertainty Aware System Identification with Universal Policies Abstract: Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world environmental parameter... |
Title: Blind leads Blind: A Zero-Knowledge Attack on Federated Learning Abstract: Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. There have been various untargeted atta... |
Title: Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets Abstract: Online ad platforms offer budget management tools for advertisers that aim to maximize the number of conversions given a budget constraint. As the volume of impressions, conversion rates and prices vary over time, these budget manag... |
Title: Identification of Flux Rope Orientation via Neural Networks Abstract: Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on ... |
Title: Multi-level Latent Space Structuring for Generative Control Abstract: Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity. We propose to leverage the StyleGAN generative architecture to devise a new truncation technique, ba... |
Title: Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness Abstract: We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1... |
Title: Compute Trends Across Three Eras of Machine Learning Abstract: Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily quantified factor - compute. We show that before 2010 training comput... |
Title: Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data Abstract: Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient descent. To b... |
Title: Improving Image-recognition Edge Caches with a Generative Adversarial Network Abstract: Image recognition is an essential task in several mobile applications. For instance, a smartphone can process a landmark photo to gather more information about its location. If the device does not have enough computational re... |
Title: Formalization of a Stochastic Approximation Theorem Abstract: Stochastic approximation algorithms are iterative procedures which are used to approximate a target value in an environment where the target is unknown and direct observations are corrupted by noise. These algorithms are useful, for instance, for root... |
Title: High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques Abstract: As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chro... |
Title: Private Adaptive Optimization with Side Information Abstract: Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential privacy, as the noise added to ensure privacy reduces the effectivenes... |
Title: Robust Deep Semi-Supervised Learning: A Brief Introduction Abstract: Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient. Recently, SSL with deep models has proven to be successful on standard benchma... |
Title: Uncalibrated Models Can Improve Human-AI Collaboration Abstract: In many practical applications of AI, an AI model is used as a decision aid for human users. The AI provides advice that a human (sometimes) incorporates into their decision-making process. The AI advice is often presented with some measure of "con... |
Title: RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework Abstract: We tackle the problem of image inpainting in the remote sensing domain. Remote sensing images possess high resolution and geographical variations, that render the conventional inpainting methods less effective. This further entails th... |
Title: Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schr\"{o}dinger's Equation Abstract: Given their ability to effectively learn non-linear mappings and perform fast inference, deep neural networks (NNs) have been proposed as a viable alternative to trad... |
Title: Coupling Online-Offline Learning for Multi-distributional Data Streams Abstract: The distributions of real-life data streams are usually nonstationary, where one exciting setting is that a stream can be decomposed into several offline intervals with a fixed time horizon but different distributions and an out-of-... |
Title: What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? Abstract: Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require su... |
Title: Robust Learning from Observation with Model Misspecification Abstract: Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorithms, they impose the somewhat unrealistic requirement that the ... |
Title: Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam Abstract: 1-bit gradient compression and local steps are two representative techniques that enable drastic communication reduction in distributed SGD. Their benefits, however, remain an open question on Adam-based large model pre-training ... |
Title: Deep Performer: Score-to-Audio Music Performance Synthesis Abstract: Music performance synthesis aims to synthesize a musical score into a natural performance. In this paper, we borrow recent advances in text-to-speech synthesis and present the Deep Performer -- a novel system for score-to-audio music performanc... |
Title: Neural NID Rules Abstract: Abstract object properties and their relations are deeply rooted in human common sense, allowing people to predict the dynamics of the world even in situations that are novel but governed by familiar laws of physics. Standard machine learning models in model-based reinforcement learnin... |
Title: Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning Abstract: Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider the problem of learnin... |
Title: Relaxing the Feature Covariance Assumption: Time-Variant Bounds for Benign Overfitting in Linear Regression Abstract: Benign overfitting demonstrates that overparameterized models can perform well on test data while fitting noisy training data. However, it only considers the final min-norm solution in linear reg... |
Title: Predicting Pollution Level Using Random Forest: A Case Study of Marilao River in Bulacan Province, Philippines Abstract: This study aims to predict the pollution level that threatens the Marilao River, located in the province of Bulacan, Philippines. The inhabitants of this area are now being exposed to pollutio... |
Title: Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers Abstract: When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indication field in the r... |
Title: Text and Image Guided 3D Avatar Generation and Manipulation Abstract: The manipulation of latent space has recently become an interesting topic in the field of generative models. Recent research shows that latent directions can be used to manipulate images towards certain attributes. However, controlling the gen... |
Title: Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning Abstract: In recent centralized nonconvex distributed learning and federated learning, local methods are one of the promising approaches to reduce communication time. However, existing... |
Title: EREBA: Black-box Energy Testing of Adaptive Neural Networks Abstract: Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determining the robustness of a DNN with respect to its energy consumpti... |
Title: Online V2X Scheduling for Raw-Level Cooperative Perception Abstract: Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communicatio... |
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