Search Results for author:
Found 22 papers, 5 papers with code
Error Causal inference for Multi-Fusion models
In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance.
Cooperative Gradient Coding for Collaborative Federated Learning
We investigate federated learning (FL) in the presence of stragglers, with emphasis on wireless scenarios where the power-constrained edge devices collaboratively train a global model on their local datasets and transmit local model updates through fading channels.
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
In ACFL, before the training, each device uploads a coded local dataset with additive noise to the central server to generate a global coded dataset under privacy preservation requirements.
Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers
For this problem, DL methods based on gradient coding have been widely investigated, which redundantly distribute the training data to the workers to guarantee convergence when some workers are stragglers.
Gradient Coding in Decentralized Learning for Evading Stragglers
In this paper, we consider a decentralized learning problem in the presence of stragglers.
MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities.
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction.
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process.
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.
Neural Network Panning: Screening the Optimal Sparse Network Before Training
Pruning on neural networks before training not only compresses the original models, but also accelerates the network training phase, which has substantial application value.
Learning the Beauty in Songs: Neural Singing Voice Beautifier
Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one.
StyleM: Stylized Metrics for Image Captioning Built with Contrastive N-grams
In this paper, we build two automatic evaluation metrics for evaluating the association between a machine-generated caption and a ground truth stylized caption: OnlyStyle and StyleCIDEr.
A Self-Explainable Stylish Image Captioning Framework via Multi-References
In this paper, we propose to build a stylish image captioning model through a Multi-style Multi modality mechanism (2M).
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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DROID: Driver-centric Risk Object Identification
Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition.
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e. g., mel-spectrogram) given a music score.
3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model
In this paper, we build a multi-style generative model for stylish image captioning which uses multi-modality image features, ResNeXt features and text features generated by DenseCap.
Decentralized Federated Learning via Mutual Knowledge Transfer
In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server.
Matching Text with Deep Mutual Information Estimation
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model.
Filling Conversation Ellipsis for Better Social Dialog Understanding
We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances.
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks
First, we decompose egocentric interactions into ego-thing and ego-stuff interaction, modeled by two GCNs.
