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Found 6 papers, 1 papers with code
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
Polynomial Approximation of Discounted Moments
We introduce an approximation strategy for the discounted moments of a stochastic process that can, for a large class of problems, approximate the true moments.
Controlling Commercial Cooling Systems Using Reinforcement Learning
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification
Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been successfully applied to many natural language processing (NLP) tasks such as text classification, question answering and so on.
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions
However, most existing ensemble attacks depend on numerous substitute models to cover the vulnerable subspace of a target model.
F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
We propose a Feature-Focusing Adversarial Training (F$^2$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns.
