Search Results for author:
Found 76 papers, 28 papers with code
Learning Structurally Stabilized Representations for Multi-modal Lossless DNA Storage
By incorporating these novel strategies, the proposed RSRL can learn highly durable, dense, and lossless representations for the subsequent storage tasks into DNA sequences.
Road Network Representation Learning with the Third Law of Geography
In response, we propose to endow road network representation with the principles of the recent Third Law of Geography.
Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization
MEGO can be used as a standalone sample-efficient optimizer or in conjunction with existing search methods as an initial solution generator.
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
We aim to predict future links within the dynamic graph while simultaneously providing causal explanations for these predictions.
Bridging the Gap Between Theory and Practice: Benchmarking Transfer Evolutionary Optimization
This paper pioneers a practical TrEO benchmark suite, integrating problems from the literature categorized based on the three essential aspects of Big Source Task-Instances: volume, variety, and velocity.
Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
However, the generalization abilities of LLMs still are unexplored to address the next POI recommendations, where users' geographical movement patterns should be extracted.
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
It consists of two novel designs: a model-agnostic diversity-oriented loss function, and a non-invasive category-aware attention mechanism.
Multi-Task Learning with Multi-Task Optimization
Multi-task learning solves multiple correlated tasks.
Precise-Physics Driven Text-to-3D Generation
However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes.
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners
To bridge this gap we propose a novel framework unifying adversarially robust similarity learning and class concept learning.
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples
Existing methods typically align probability distributions of natural and adversarial samples between teacher and student models but they overlook intermediate adversarial samples along the "adversarial path" formed by the multi-step gradient ascent of a sample towards the decision boundary.
Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation
To enhance reliability and mitigate the hallucination issue, we develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision based on the distinct mistakes made by ChatGPT on various neighbor sessions.
Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization
In this paper, we introduce a novel concept of \textit{inverse transfer} in multiobjective optimization.
PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
In this work, we propose a prior-based residual learning paradigm for fast multi-view neural surface reconstruction.
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models.
Large Language Models for Intent-Driven Session Recommendations
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions.
Generalizable Neural Physics Solvers by Baldwinian Evolution
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them.
Large Language Models as Evolutionary Optimizers
Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions.
HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning
Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting.
Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization
Moreover, given a real-time constraint of one minute, the NIE-based method can solve IBM problems with up to hundreds of thousands of nodes, which is at least one order of magnitude larger than what can be solved by existing methods.
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
Particularly, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities.
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
In particular, we focus on the practical scenario of CCMCKP, where the probability distributions of random weights are unknown but only sample data is available.
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI.
Large Language Models can be Guided to Evade AI-Generated Text Detection
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public.
Bayesian Federated Learning: A Survey
This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.
Policy Dispersion in Non-Markovian Environment
To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation.
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution.
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Hence, neuroevolution algorithms, with their superior global search capacity, may be a better choice for PINNs relative to gradient descent methods.
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms.
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Being equipped with three modules (i. e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns.
Understanding Diversity in Session-Based Recommendation
Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets.
Importance Prioritized Policy Distillation
Given such an issue, we denote the \emph{frame importance} as its contribution to the expected reward on a particular frame, and hypothesize that adapting such frame importance could benefit the performance of the distilled student policy.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison.
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction.
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data
Granger causality is a commonly used method for uncovering information flow and dependencies in a time series.
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?
Model robustness is vital for the reliable deployment of machine learning models in real-world applications.
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.
Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models
For deep learning, size is power.
Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes
This paper is the first to address the shortcoming of today's methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand.
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks.
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy.
Synthesising Audio Adversarial Examples for Automatic Speech Recognition
Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly.
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More
Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation.
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
In this paper, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs.
Multi-Party Dual Learning
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.
RNA Alternative Splicing Prediction with Discrete Compositional Energy Network
In the case of alternative splicing prediction, DCEN models mRNA transcript probabilities through its constituent splice junctions' energy values.
Multi-Space Evolutionary Search for Large-Scale Optimization
This paper thus proposes a new search paradigm, namely the multi-space evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems.
Learning Conjoint Attentions for Graph Neural Nets
Given the novel Conjoint Attention strategies, we then propose Graph conjoint attention networks (CATs) that can learn representations embedded with significant latent features deemed by the Conjoint Attentions.
Can Transfer Neuroevolution Tractably Solve Your Differential Equations?
In the context of solving differential equations, we are faced with the problem of finding globally optimum parameters of the network, instead of being concerned with out-of-sample generalization.
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
Under such settings, existing transfer evolutionary optimization frameworks grapple with simultaneously satisfying two important quality attributes, namely (1) scalability against a growing number of source tasks and (2) online learning agility against sparsity of relevant sources to the target task of interest.
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems.
Graph Joint Attention Networks
In this paper, we propose Graph Joint Attention Networks (JATs) to address the aforementioned challenge.
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability.
Adversary Agnostic Robust Deep Reinforcement Learning
Motivated by this finding, we propose a new policy distillation loss with two terms: 1) a prescription gap maximization loss aiming at simultaneously maximizing the likelihood of the action selected by the teacher policy and the entropy over the remaining actions; 2) a corresponding Jacobian regularization loss that minimizes the magnitude of the gradient with respect to the input state.
CoCon: A Self-Supervised Approach for Controlled Text Generation
While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, there is still a lack of more precise control over its content at the word- and phrase-level.
Deep Latent-Variable Kernel Learning
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model.
Heterogeneous Representation Learning: A Review
The real-world data usually exhibits heterogeneous properties such as modalities, views, or resources, which brings some unique challenges wherein the key is Heterogeneous Representation Learning (HRL) termed in this paper.
What it Thinks is Important is Important: Robustness Transfers through Input Gradients
Learned weights of models robust to such perturbations are previously found to be transferable across different tasks but this applies only if the model architecture for the source and target tasks is the same.
Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks
Existing defenses are effective under certain conditions such as a small size of the poison pattern, knowledge about the ratio of poisoned training samples or when a validated clean dataset is available.
A Multi-Task Gradient Descent Method for Multi-Label Learning
Multi-label learning studies the problem where an instance is associated with a set of labels.
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples.
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.
A Survey on Multi-output Learning
Multi-output learning aims to simultaneously predict multiple outputs given an input.
AIR5: Five Pillars of Artificial Intelligence Research
In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the fields of artificial intelligence (AI) and computational intelligence (CI); with the latter focusing on algorithms that are inspired by various natural phenomena.
Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Vulnerabilities
In particular, we propose a novel approach of sequential learning of smart contract vulnerabilities using machine learning --- long-short term memory (LSTM) --- that perpetually learns from an increasing number of contracts handled over time, leading to safer smart contracts.
Cryptography and Security
Large-scale Heteroscedastic Regression via Gaussian Process
To improve the scalability, we first develop a variational sparse inference algorithm, named VSHGP, to handle large-scale datasets.
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness.
When Gaussian Process Meets Big Data: A Review of Scalable GPs
The review of scalable GPs in the GP community is timely and important due to the explosion of data size.
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities.
Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
Co-evolutionary multi-task learning for dynamic time series prediction
In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction.
Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme.
Adaptive Subgradient Methods for Online AUC Maximization
Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple online gradient descent approaches that fail to exploit the geometrical knowledge of the data observed during the online learning process, and thus could suffer from relatively larger regret.
