Search Results for author:
Found 27 papers, 5 papers with code
Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation
In this paper, we put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD), which can detect objects beyond training categories without costly collecting new labeled data.
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning
The Airbnb search system grapples with many unique challenges as it continues to evolve.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
The performance of OVD greatly relies on the quality of class-agnostic region proposals and pseudo-labels for novel object categories.
Explainable Analysis of Deep Learning Methods for SAR Image Classification
Deep learning methods exhibit outstanding performance in synthetic aperture radar (SAR) image interpretation tasks.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Deep Natural Language Processing for LinkedIn Search Systems
Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).
Deep Search Query Intent Understanding
Understanding a user's query intent behind a search is critical for modern search engine success.
DeText: A Deep Text Ranking Framework with BERT
Ranking is the most important component in a search system.
Efficient Neural Query Auto Completion
On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
Targeted Attack for Deep Hashing based Retrieval
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization
The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience.
Abstractive Multi-Document Summarization via Phrase Selection and Merging
We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases.
