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Found 26 papers, 14 papers with code
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning
Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance.
WavMark: Watermarking for Audio Generation
Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice using just a few seconds of recording while maintaining a high level of realism.
REAL: A Representative Error-Driven Approach for Active Learning
Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training.
Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
The widely used practice is to build task-specific or even dataset-specific solutions, which are hard to generalize and disable the opportunities of knowledge sharing that can be learned from different datasets and multiple tasks.
ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4. 7 points (85. 6% vs. 80. 9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1. 5 points (90. 6% vs. 92. 1%).
Ranked #2 on
Table-based Fact Verification
on TabFact
Human Pose Driven Object Effects Recommendation
In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion.
Domain Adaptation for Deep Entity Resolution: A Design Space Exploration
Entity resolution (ER) is a core problem of data integration.
Ranked #2 on
Entity Resolution
on WDC Watches-small
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network
Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process.
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM).
A Benchmark for Voice-Face Cross-Modal Matching and Retrieval
In this paper, a baseline evaluation framework is proposed for voice-face matching and retrieval tasks.
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and then learning a model to reconstruct the original tuple.
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches.
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in hyperspectral image classification.
Voice-Face Cross-modal Matching and Retrieval: A Benchmark
It achieves state-of-the-art performance with various performance metrics on different tasks and with high test confidence on large scale datasets, which can be taken as a baseline for the follow-up research.
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
Subword-level Composition Functions for Learning Word Embeddings
Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries.
Analogical Reasoning on Chinese Morphological and Semantic Relations
Analogical reasoning is effective in capturing linguistic regularities.
Initializing Convolutional Filters with Semantic Features for Text Classification
Convolutional Neural Networks (CNNs) are widely used in NLP tasks.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
The number of word embedding models is growing every year.
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics
The existing word representation methods mostly limit their information source to word co-occurrence statistics.
Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification
We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words.
Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews
Many document embeddings methods have been proposed to capture semantics, but they still can't outperform bag-of-ngram based methods on this task.
