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Found 19 papers, 3 papers with code
EDIS: Entity-Driven Image Search over Multimodal Web Content
Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion.
Beyond Object Recognition: A New Benchmark towards Object Concept Learning
To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels.
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
We argue that such a network is a powerful component for many possible multiagent algorithms.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
DynaConF: Dynamic Forecasting of Non-Stationary Time-Series
Deep learning models have shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence.
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games.
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.
Code Smells in Machine Learning Systems
We found that our newly identified code smells are prevalent and impactful on the maintenance of DL systems from the developer's perspective.
NeuPL: Neural Population Learning
Learning in strategy games (e. g. StarCraft, poker) requires the discovery of diverse policies.
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance.
Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction
Recently, a series of deep learning based segmentation methods have been proposed to improve the quality of raw 3D optical image stacks by removing noises and restoring neuronal structures from low-contrast background.
Drug-Target Interaction Prediction with Graph Attention networks
For better learning and interpreting the DTI topological structure and the similarity, it is desirable to have methods specifically for predicting interactions from the graph structure.
Partial Graph Reasoning for Neural Network Regularization
This add-on graph regularizes the network during training and can be completely skipped during inference.
From Motor Control to Team Play in Simulated Humanoid Football
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
On statistical inference when fixed points of belief propagation are unstable
For instance, in a planted constraint satisfaction problem such as planted 3-SAT, the clauses are sparse observations from which the hidden assignment is to be recovered.
Community Detection
Data Structures and Algorithms
Probability
Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images
One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation.
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
We develop a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.
