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Found 31 papers, 15 papers with code
Bag-of-Words Baselines for Semantic Code Search
The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language.
A Little Bit Is Worse Than None: Ranking with Limited Training Data
Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search.
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking
For example, on MS MARCO Passage v1, our method yields an average candidate set size of 27 out of 1, 000 which increases the reranking speed by about 37 times, while the MRR@10 is greater than a pre-specified value of 0. 38 with about 90% empirical coverage and the empirical baselines fail to provide such guarantee.
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
Towards Best Practices for Training Multilingual Dense Retrieval Models
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research.
Generative Pre-Trained Transformers for Biologically Inspired Design
Biological systems in nature have evolved for millions of years to adapt and survive the environment.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
Multi-channel deep convolutional neural networks for multi-classifying thyroid disease
Therefore, this study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease.
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models
However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs.
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
ELUE is dedicated to depict the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.
Tuning Confidence Bound for Stochastic Bandits with Bandit Distance
We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others.
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations.
Answer Complex Questions: Path Ranker Is All You Need
Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals
Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving.
IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task.
Early Exiting with Ensemble Internal Classifiers
In this paper, we show that a novel objective function for the training of the ensemble internal classifiers can be naturally induced from the perspective of ensemble learning and information theory.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.
Lite-FPN for Keypoint-based Monocular 3D Object Detection
3D object detection with a single image is an essential and challenging task for autonomous driving.
A novel multimodal fusion network based on a joint coding model for lane line segmentation
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection
3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control.
Diverse Knowledge Distillation for End-to-End Person Search
Person search aims to localize and identify a specific person from a gallery of images.
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation.
Peer-to-Peer Localization for Single-Antenna Devices
In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy.
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Object detection methods are widely adopted for computer-aided diagnosis using medical images.
