Search Results for author:
Found 23 papers, 9 papers with code
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
Finally, we showcase how ingredient-based attribute extraction contributes to enhancing the explainability of beauty recommendations.
Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on
An intelligent approach for product discovery is required to enhance the makeup shopping experience to make it more convenient and satisfying.
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks
Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints.
ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages
Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages.
Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality
To make accurate recommendations, it is crucial to infer both the product attributes and the product specific facial features such as skin conditions or tone.
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research.
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Our method of semantically meaningful image augmentation for object detection via language grounding, SemAug, starts by calculating semantically appropriate new objects that can be placed into relevant locations in the image (the what and where problems).
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm.
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data
Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data.
Extending Momentum Contrast with Cross Similarity Consistency Regularization
Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs.
Spatial Cross-Attention Improves Self-Supervised Visual Representation Learning
Unsupervised representation learning methods like SwAV are proved to be effective in learning visual semantics of a target dataset.
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach
In complex problems with large state and action spaces, it is advantageous to extend MAPG methods to use higher-level actions, also known as options, to improve the policy search efficiency.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
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E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models
As such, it can be applied to black-box pre-trained models without a need for architectural manipulations, reassembling of modules, or re-training.
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data
The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models.
Model Composition: Can Multiple Neural Networks Be Combined into a Single Network Using Only Unlabeled Data?
As an attempt to mitigate this dilemma, this paper investigates the idea of combining multiple trained neural networks using unlabeled data.
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples
Through an extensive set of experiments, we demonstrate the usefulness of the repainted examples in training, for the tasks of image classification (ImageNet) and object detection (COCO), over several state-of-the-art network architectures at different capacities, and across different data availability regimes.
EBJR: Energy-Based Joint Reasoning for Adaptive Inference
To this end, we propose an Energy-Based Joint Reasoning (EBJR) framework that adaptively distributes the samples between shallow and deep models to achieve an accuracy close to the deep model, but latency close to the shallow one.
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
At the same time, large amounts of input data are collected at the edge of cloud.
SimROD: A Simple Adaptation Method for Robust Object Detection
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).
Revisiting Knowledge Distillation for Object Detection
Extensive experiments demonstrate improvements over existing object detection distillation algorithms.
A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)
To this end we propose a learning-based visual saliency fusion method for HDR content (LVBS-HDR) to combine various visual saliency features.
A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video (LBVS-3D)
Our model starts with a rough segmentation and quantifies several intuitive observations such as the effects of visual discomfort level, depth abruptness, motion acceleration, elements of surprise, size and compactness of the salient regions, and emphasizing only a few salient objects in a scene.
