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.
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.
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.
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.
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.
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming.
3 code implementations • 13 Dec 2022 • Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Guo, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
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.
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
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.
Entity resolution (ER) is a core problem of data integration.
Ranked #2 on Entity Resolution on WDC Watches-small
Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process.
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM).
In this paper, a baseline evaluation framework is proposed for voice-face matching and retrieval tasks.
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.
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.
We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches.
Deep learning methods have played a more and more important role in hyperspectral image classification.
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.
Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries.
The number of word embedding models is growing every year.
The existing word representation methods mostly limit their information source to word co-occurrence statistics.
We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words.
Many document embeddings methods have been proposed to capture semantics, but they still can't outperform bag-of-ngram based methods on this task.