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Found 14 papers, 7 papers with code
Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics
Quantitatively measuring the impact-related aspects of scientific, engineering, and technological (SET) innovations is a fundamental problem with broad applications.
Fooling the Textual Fooler via Randomizing Latent Representations
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave.
Walk-and-Relate: A Random-Walk-based Algorithm for Representation Learning on Sparse Knowledge Graphs
As these models only use the observed triplets to estimate the embeddings, they are prone to suffer through data sparsity that usually occurs in the real-world knowledge graphs, i. e., the lack of enough triplets per entity.
An Embedding-Based Grocery Search Model at Instacart
To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method.
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs
To address this question, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)', which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away.
Distant-Supervised Slot-Filling for E-Commerce Queries
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.).
Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space
We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small dimensional feature space representing such a manifold than on the original pixel-space.
Regression via Implicit Models and Optimal Transport Cost Minimization
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x, z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$.
Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance
In this paper, we show that the high sample-complexity requirement often results in sub-optimal retrieval performance of the adversarial hashing methods.
Targeted display advertising: the case of preferential attachment
We present an experimental analysis on the historical logs of a major display advertising platform (https://www. criteo. com/).
CAWA: An Attention-Network for Credit Attribution
Experiments on the credit attribution task on a variety of datasets show that the sentence class labels generated by CAWA outperform the competing approaches.
Intent term selection and refinement in e-commerce queries
Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.
Text segmentation on multilabel documents: A distant-supervised approach
Segmenting text into semantically coherent segments is an important task with applications in information retrieval and text summarization.
Distributed representation of multi-sense words: A loss-driven approach
Word2Vec's Skip Gram model is the current state-of-the-art approach for estimating the distributed representation of words.
