Search Results for author:
Found 17 papers, 5 papers with code
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way.
Shuffle & Divide: Contrastive Learning for Long Text
We propose a self-supervised learning method for long text documents based on contrastive learning.
Simulation-guided Beam Search for Neural Combinatorial Optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images.
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.
Matrix Encoding Networks for Neural Combinatorial Optimization
A popular approach is to use a neural net to compute on the parameters of a given CO problem and extract useful information that guides the search for good solutions.
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages.
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training.
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-ofthe-art results in sentence-pair regressions such as semantic textual similarity (STS) and natural language inference (NLI).
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver.
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art.
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games.
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression.
Adversarial nets with perceptual losses for text-to-image synthesis
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text.
Multimodal Sparse Coding for Event Detection
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality.
Multimodal sparse representation learning and applications
In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities.
