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Found 24 papers, 13 papers with code
Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language
Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility.
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards
Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards.
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport
Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits.
EvoJAX: Hardware-Accelerated Neuroevolution
Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters.
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms.
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts
Evolutionary algorithms have been used in the digital art scene since the 1970s.
Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation
The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches.
InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations.
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter.
Fast and Accurate Network Embeddings via Very Sparse Random Projection
Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimization-free method for dimension reduction.
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS).
Learning Bilingual Word Embeddings Using Lexical Definitions
Bilingual word embeddings, which representlexicons of different languages in a shared em-bedding space, are essential for supporting se-mantic and knowledge transfers in a variety ofcross-lingual NLP tasks.
Latent Domain Transfer: Crossing modalities with Bridging Autoencoders
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex.
Latent Translation: Crossing Modalities by Bridging Generative Models
We compare to state-of-the-art techniques and find that a straight-forward variational autoencoder is able to best bridge the two generative models through learning a shared latent space.
Enhanced Network Embeddings via Exploiting Edge Labels
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network.
Social and Information Networks Physics and Society
A Transfer-Learnable Natural Language Interface for Databases
In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database.
On2Vec: Embedding-based Relation Prediction for Ontology Population
Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem.
Learning to Represent Bilingual Dictionaries
Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages.
Simple Neologism Based Domain Independent Models to Predict Year of Authorship
We present domain independent models to date documents based only on neologism usage patterns.
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions.
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data.
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
With quantitative analysis and case studies we demonstrate that our efforts lead to a stable and high-quality model.
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs.
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On the Convergent Properties of Word Embedding Methods
Do word embeddings converge to learn similar things over different initializations?
