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Found 21 papers, 9 papers with code
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
The significant role of division of labor (DOL) in promoting cooperation is widely recognized in real-world applications. Many cooperative multi-agent reinforcement learning (MARL) methods have incorporated the concept of DOL to improve cooperation among agents. However, the tasks used in existing testbeds typically correspond to tasks where DOL is often not a necessary feature for achieving optimal policies. Additionally, the full utilize of DOL concept in MARL methods remains unrealized due to the absence of appropriate tasks. To enhance the generality and applicability of MARL methods in real-world scenarios, there is a necessary to develop tasks that demand multi-agent DOL and cooperation. In this paper, we propose a series of tasks designed to meet these requirements, drawing on real-world rules as the guidance for their design. We guarantee that DOL and cooperation are necessary condition for completing tasks and introduce three factors to expand the diversity of proposed tasks to cover more realistic situations. We evaluate 10 cooperative MARL methods on the proposed tasks. The results indicate that all baselines perform poorly on these tasks. To further validate the solvability of these tasks, we also propose simplified variants of proposed tasks. Experimental results show that baselines are able to handle these simplified variants, providing evidence of the solvability of the proposed tasks. The source files is available at https://github. com/Yurui-Li/CTC.
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral Analysis
In this paper, we propose SpectralKD, a novel unified analytical framework that offers deeper insights into ViTs and optimizes KD via spectral analysis.
Ranked #7 on
Knowledge Distillation
on ImageNet
Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation
Visual Tokenization, trained by self-supervised reconstructive learning, compresses visual data by codebook in patch-level with marginal information loss, but the visual tokens does not have semantic meaning.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
Secondly, existing EEG foundation models have limited generalizability on a wide range of downstream BCI tasks due to varying formats of EEG data, making it challenging to adapt to.
A Framework For Image Synthesis Using Supervised Contrastive Learning
The semantic label in priory describes the inherent distribution pattern with underlying cross-image relationships, which is supplement to the text description for understanding the full characteristics of image.
RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation
Additionally, we design new baselines that incorporate urban characteristics, such as road orientation and building density, in the process of generating road networks and building layouts using the RoBus dataset, enhancing the practicality of automated urban design.
Generalizable Sleep Staging via Multi-Level Domain Alignment
In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets.
Multi-Depth Branch Network for Efficient Image Super-Resolution
We visualize this process using feature maps, and further demonstrate the rationality and effectiveness of this design using proposed novel Fourier spectral analysis methods.
A detail-enhanced sampling strategy in Hadamard single-pixel imaging
Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost.
Thompson Sampling for Unimodal Bandits
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e. g. StarCraft and poker.
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning
Further, we observe that Sync-Switch achieves 3. 8% higher converged accuracy with just 1. 23X the training time compared to training with ASP.
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
However, it is challenging to determine the appropriate cluster configuration---e. g., server type and number---for different training workloads while balancing the trade-offs in training time, cost, and model accuracy.
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models
Leveraging Perseus, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving led to up to 12% cost reduction.
Inverse Reinforcement Learning with Multiple Ranked Experts
We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators of varying performance.
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning.
TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems.
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can't.
Speeding up Deep Learning with Transient Servers
Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers.
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning.
Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.
