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Found 20 papers, 3 papers with code
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning
Our method forces the network to learn the necessary features for all the words in the input, which alleviates the shortcut learning problem.
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service
There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Divide and Ensemble: Progressively Learning for the Unknown
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
Learning to Personalize Recommendation based on Customers' Shopping Intents
In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations.
FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration
We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters.
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features.
Learning Personalized Page Content Ranking Using Customer Representation
We propose a deep learning based bandit algorithm that incorporates historical shopping behavior, customer latent shopping goals, and the correlation between customers and content categories.
Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection
Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications.
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.
Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR
The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages.
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively.
Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation
Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model.
Efficient Medical Image Segmentation Based on Knowledge Distillation
To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.
Two-stage Training for Learning from Label Proportions
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.
Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation
Open-domain multi-turn conversations mainly have three features, which are hierarchical semantic structure, redundant information, and long-term dependency.
OT-LLP: Optimal Transport for Learning from Label Proportions
Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.
A Novel Large-scale Ordinal Regression Model
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels.
Label Consistent Fisher Vectors for Supervised Feature Aggregation
In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years.
