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Found 41 papers, 3 papers with code
LLMs Instruct LLMs:An Extraction and Editing Method
The interest in updating Large Language Models (LLMs) without retraining from scratch is substantial, yet it comes with some challenges. This is especially true for situations demanding complex reasoning with limited samples, a scenario we refer to as the Paucity-Constrained Complex Reasoning Adaptation for LLMs (PCRA-LLM). Traditional methods like Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation (RAG) are inadequate for this critical issue, particularly evident in our exploration of a specific medical context that epitomize the PCRA-LLM's distinct needs. To address the issue, we propose a Sequential Fusion method to incorporate knowledge from complex context into LLMs.
ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
In specific, ROG$_{PL}$ consists of two modules, i. e., denoising via label propagation and open-set prototype learning via regions.
Unsupervised multiple choices question answering via universal corpus
Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain.
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data.
Affordable Generative Agents
The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents.
More Agents Is All You Need
We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated.
Multi-level Cross-modal Alignment for Image Clustering
Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model.
How Does China's Household Portfolio Selection Vary with Financial Inclusion?
Portfolio underdiversification is one of the most costly losses accumulated over a household's life cycle.
$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment.
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions.
Learning for Transductive Threshold Calibration in Open-World Recognition
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR).
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in real-world applications.
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits.
Lightweight Monocular Depth Estimation
Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images.
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics.
A Survey for Efficient Open Domain Question Answering
Thus, a trade-off between accuracy, memory consumption and processing speed is pursued.
Semantic-Enhanced Image Clustering
In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model.
Communication-Efficient Collaborative Best Arm Identification
We investigate top-$m$ arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function.
Parallel Best Arm Identification in Heterogeneous Environments
In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments and they want to learn in parallel an objective function in the aggregated environment.
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well.
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis
In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner.
Deep Unsupervised Hashing with Latent Semantic Components
Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited.
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement
After that, the second stage is applied to further suppress the residual noise components and estimate the clean phase by a complex spectral mapping network, which is a pure complex-valued network composed of complex 2D convolution/deconvolution and complex temporal-frequency attention blocks.
Learning to Cluster via Same-Cluster Queries
We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries.
Deep Self-Adaptive Hashing for Image Retrieval
Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning.
Batched Thompson Sampling for Multi-Armed Bandits
We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small number of policy changes (or, batches).
Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade.
Batched Coarse Ranking in Multi-Armed Bandits
We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we have a set of arms each of which is associated with an unknown distribution.
A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation.
Multinomial Logit Bandit with Low Switching Cost
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
Collaborative Top Distribution Identifications with Limited Interaction
We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants.
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits
Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning.
Distinct Sampling on Streaming Data with Near-Duplicates
In this paper we study how to perform distinct sampling in the streaming model where data contain near-duplicates.
Data Structures and Algorithms
A Practical Algorithm for Distributed Clustering and Outlier Detection
We study the classic $k$-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers.
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously.
Adaptive Multiple-Arm Identification
We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game.
Prediction of Sea Surface Temperature using Long Short-Term Memory
We formulate the SST prediction problem as a time series regression problem.
Communication-Optimal Distributed Clustering
We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site.
EmbedJoin: Efficient Edit Similarity Joins via Embeddings
Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems.
Databases
