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Found 16 papers, 2 papers with code
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
Streaming Align-Refine for Non-autoregressive Deliberation
We propose a streaming non-autoregressive (non-AR) decoding algorithm to deliberate the hypothesis alignment of a streaming RNN-T model.
Continual Learning for CTR Prediction: A Hybrid Approach
To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.
Polarized skylight orientation determination artificial neural network
This paper proposes an artificial neural network to determine orientation using polarized skylight.
Deep Position-wise Interaction Network for CTR Prediction
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems.
Learning Word-Level Confidence For Subword End-to-End ASR
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).
Transformer Based Deliberation for Two-Pass Speech Recognition
In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring.
Realtime CNN-based Keypoint Detector with Sobel Filter and CNN-based Descriptor Trained with Keypoint Candidates
We design Gaussian loss for the training process of SobelNet to detect corner points as keypoints.
Textual Echo Cancellation
In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings.
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
Deliberation Model Based Two-Pass End-to-End Speech Recognition
End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models.
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
Contextual automatic speech recognition, i. e., biasing recognition towards a given context (e. g. user's playlists, or contacts), is challenging in end-to-end (E2E) models.
Adversarial Training for Multilingual Acoustic Modeling
In this work, we apply the domain adversarial network to encourage the shared layers of a multilingual model to learn language-invariant features.
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation
We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context.
