Search Results for author:
Found 18 papers, 4 papers with code
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage.
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II.
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations research.
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
This work is an attempt to challenge a common practice in the design of existing MOEAs.
Retrieval-Augmented Classification with Decoupled Representation
Retrieval augmented methods have shown promising results in various classification tasks.
Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models
We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT.
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality
The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic.
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks
It broadcasts the global model in the server to each client and produces pseudo data for clients so that knowledge from the global model can be explored to enhance few-shot learning of each client model.
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework
Knowledge distillation (KD) has been widely used for model compression and knowledge transfer.
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets.
Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool.
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.
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.
Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to deal with noise.
Tencent Neural Machine Translation Systems for WMT18
We participated in the WMT 2018 shared news translation task on English↔Chinese language pair.
Neural Machine Translation with Decoding History Enhanced Attention
Neural machine translation with source-side attention have achieved remarkable performance.
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds.
