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Found 9 papers, 0 papers with code
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack
“Explanations can increase the transparency of neural networks and make them more trustworthy.
A Robustly Optimized BERT Pre-training Approach with Post-training
“In the paper we present a ‘pre-training’+‘post-training’+‘fine-tuning’ three-stage paradigm which is a supplementary framework for the standard ‘pre-training’+‘fine-tuning’ languagemodel approach.
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method
In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods.
Abstains from Prediction: Towards Robust Relation Extraction in Real World
“Supervised learning is a classic paradigm of relation extraction (RE).
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain
Having realized this we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages.
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension
“The irrelevant information in documents poses a great challenge for machine reading compre-hension (MRC).
Intelligent reflecting surface aided wireless networks-Harris Hawks optimization for beamforming design
Intelligent Reflecting Surface (IRS) is envisioned to be a promising green and cost-effective solution to enhance wireless network performance by smartly reconfiguring the signal propagation.
Networking and Internet Architecture
End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings
The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search.
Action Recognition based on Subdivision-Fusion Model
In the subdivision stage of the proposed SFM, samples in each category are clustered.
