Search Results for author:
Found 16 papers, 2 papers with code
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising
Multiple content providers rely on native advertisement for revenue by placing ads within the organic content of their pages.
On Learning Sparsely Used Dictionaries from Incomplete Samples
Second, we propose an initialization algorithm that utilizes a small number of extra fully observed samples to produce such a coarse initial estimate.
Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work.
Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time.
Rank-to-engage: New Listwise Approaches to Maximize Engagement
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e. g., click-through-rate, average engagement time etc.)
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently.
Noisy Inductive Matrix Completion Under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks where the underlying matrix is following an IMC model i. e., it is formed by a mixing matrix (a priori unknown) sandwiched between two known feature matrices.
Distributed Representations for Biological Sequence Analysis
Biological sequence comparison is a key step in inferring the relatedness of various organisms and the functional similarity of their components.
Noisy Matrix Completion under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks where the goal is to estimate a matrix from observations obtained at a subset of its entries, each of which is subject to random noise or corruption.
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowledge on the signal, interference, and noise is available.
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adaptive linear observations, provided that the signals possess a sparse representation in some basis.
