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Found 24 papers, 6 papers with code
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?
Comparing with the FiD reader, this approach matches its accuracy by utilizing just 18. 32% of its reader inference cost and also outperforms it by achieving up to 55. 10% accuracy on NQ Open.
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i. e., the legitimate targets for rebalancing operations can vary over time.
A Study on the Efficiency and Generalization of Light Hybrid Retrievers
Moreover, to further reduce the memory, we introduce a lighter dense retriever (LITE) which is jointly trained on contrastive learning and knowledge distillation from DrBoost.
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
BioTABQA: Instruction Learning for Biomedical Table Question Answering
To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information.
Neural Retriever and Go Beyond: A Thesis Proposal
First, we introduce methods to address the abovementioned issues of neural retrievers from three angles, new model architectures, IR-oriented pretraining tasks, and generating large scale training data.
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
Recently, instructional prompts have shown significant improvement towards multi-task generalization; however, the effect of instructional prompts and Multi-Task Learning (MTL) has not been systematically studied in the biomedical domain.
Improving Contrastive Learning with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness
However, the effect of data modification on adversarial robustness remains unclear.
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e. g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoder-only PrLMs (e. g., RoBERTa).
Improving Biomedical Information Retrieval with Neural Retrievers
We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.
Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search
Shared e-mobility services have been widely tested and piloted in cities across the globe, and already woven into the fabric of modern urban planning.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection. The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28\% in terms of exact matching of relevant sentences on the HotpotQA dataset.
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge.
Deep Signature FBSDE Algorithm
We propose a deep signature/log-signature FBSDE algorithm to solve forward-backward stochastic differential equations (FBSDEs) with state and path dependent features.
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks
GQA (CITATION) is a dataset for real-world visual reasoning and compositional question answering.
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks
GQA~\citep{hudson2019gqa} is a dataset for real-world visual reasoning and compositional question answering.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
Unitary Approximate Message Passing for Sparse Bayesian Learning
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
Can Transformers Reason About Effects of Actions?
A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditions implying a conclusion.
Strong Equivalence for LPMLN Programs
We show that the verification of strong equivalence in LPMLN can be reduced to equivalence checking in classical logic via a reduct and choice rules as well as to equivalence checking under the "soft" logic of here-and-there.
Strong equivalence for $\rm LP^{MLN}$ programs
Strong equivalence is a well-studied and important concept in answer set programming (ASP).
Logic in Computer Science
Demand Prediction for Electric Vehicle Sharing
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe.
