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Found 28 papers, 12 papers with code
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis
Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts.
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes.
EDGE++: Improved Training and Sampling of EDGE
Recently developed deep neural models like NetGAN, CELL, and Variational Graph Autoencoders have made progress but face limitations in replicating key graph statistics on generating large graphs.
On Separate Normalization in Self-supervised Transformers
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains.
Kriging Convolutional Networks
Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends.
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e. g. sample hardness.
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
The empirical study shows that EDGE is much more efficient than competing methods and can generate large graphs with thousands of nodes.
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations
Recently the Transformer structure has shown good performances in graph learning tasks.
NVDiff: Graph Generation through the Diffusion of Node Vectors
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures.
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
In this work, we convert graph pruning to a problem of node relabeling and then relax it to a differentiable problem.
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.
Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation
We show that annotation performance, for ESP and other models, is a strong function of the number of molecules in the candidate set and their similarity to the target molecule.
Predicting Physics in Mesh-reduced Space with Temporal Attention
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes.
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction
We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning.
Ladder Polynomial Neural Networks
Particularly this work constructs polynomial feedforward neural networks using the product activation, a new activation function constructed from multiplications.
Stochastic Iterative Graph Matching
In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.
Modeling Graph Node Correlations with Neighbor Mixture Models
We propose a new model, the Neighbor Mixture Model (NMM), for modeling node labels in a graph.
GAN Ensemble for Anomaly Detection
Motivated by the observation that GAN ensembles often outperform single GANs in generation tasks, we propose to construct GAN ensembles for anomaly detection.
Learning graph representations of biochemical networks and its application to enzymatic link prediction
We develop in this work a technique, Enzymatic Link Prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules.
Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
Annotation results are in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures.
SecureGBM: Secure Multi-Party Gradient Boosting
Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data.
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem.
Ranked #13 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network.
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes
Many recent inference methods approximate the posterior distribution with a simpler distribution defined on a small number of inducing points.
Zero-Inflated Exponential Family Embeddings
In this paper, we develop zero-inflated embeddings, a new embedding method that is designed to learn from sparse observations.
Transductive Optimization of Top k Precision
This paper introduces a new approach, Transductive Top K (TTK), that seeks to minimize the hinge loss over all training instances under the constraint that exactly $k$ test instances are predicted as positive.
Gaussian Approximation of Collective Graphical Models
The Collective Graphical Model (CGM) models a population of independent and identically distributed individuals when only collective statistics (i. e., counts of individuals) are observed.
