Search Results for author:
Found 16 papers, 8 papers with code
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Its objective is to return a set of $k$ data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set.
Efficient Multi-Vector Dense Retrieval Using Bit Vectors
This paper proposes ``Efficient Multi-Vector dense retrieval with Bit vectors'' (EMVB), a novel framework for efficient query processing in multi-vector dense retrieval.
Bridging Dense and Sparse Maximum Inner Product Search
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval.
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier.
Neural Network Compression using Binarization and Few Full-Precision Weights
Quantization and pruning are two effective Deep Neural Networks model compression methods.
Efficient and Effective Tree-based and Neural Learning to Rank
We believe that by understanding the fundamentals underpinning these algorithmic and data structure solutions for containing the contentious relationship between efficiency and effectiveness, one can better identify future directions and more efficiently determine the merits of ideas.
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages.
Caching Historical Embeddings in Conversational Search
Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.
ILMART: Interpretable Ranking with Constrained LambdaMART
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models.
An Optimal Algorithm for Finding Champions in Tournament Graphs
In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches.
Learning Early Exit Strategies for Additive Ranking Ensembles
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees.
Dynamic Hard Pruning of Neural Networks at the Edge of the Internet
Results show that DynHP compresses a NN up to $10$ times without significant performance drops (up to $3. 5\%$ additional error w. r. t.
Query-level Early Exit for Additive Learning-to-Rank Ensembles
In this paper, we investigate the novel problem of \textit{query-level early exiting}, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble.
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking.
Expansion via Prediction of Importance with Contextualization
We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches.
Training Curricula for Open Domain Answer Re-Ranking
We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process.
