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Arxiv
1w
83
Image Credit: Arxiv
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
- This paper explores the application of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks (WLANs).
- The study focuses on the practical scenario where agents adopt heterogeneously value-based or policy-based reinforcement learning algorithms to train the model.
- The researchers propose a heterogeneous MARL training framework called QPMIX, which enables collaboration among heterogeneous agents through centralized training and distributed execution.
- Simulation results demonstrate that the QPMIX algorithm improves network throughput, mean delay, delay jitter, and collision rates compared to conventional carrier-sense multiple access with collision avoidance in saturated traffic scenarios.
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Arxiv
1w
79
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A Survey on Inference Optimization Techniques for Mixture of Experts Models
- A new survey analyzes inference optimization techniques for Mixture of Experts (MoE) models.
- The survey categorizes optimization approaches into model-level, system-level, and hardware-level optimizations.
- Model-level optimizations include architectural innovations, compression techniques, and algorithm improvements.
- System-level optimizations investigate distributed computing approaches, load balancing mechanisms, and efficient scheduling algorithms.
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Arxiv
1w
393
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Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
- Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a pivotal component.
- Existing approaches incorporate neighborhood similarity into node-wise temperature scaling techniques, but this assumption does not hold universally and can lead to sub-optimal calibration.
- The new approach called Simi-Mailbox categorizes nodes by both neighborhood similarity and their own confidence, allowing fine-grained calibration using group-specific temperature scaling.
- Extensive experiments demonstrate the effectiveness of Simi-Mailbox, achieving up to 13.79% error reduction compared to uncalibrated GNN predictions.
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Arxiv
1w
305
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Distributionally Robust Policy Learning under Concept Drifts
- Distributionally robust policy learning aims to find a policy that performs well under the worst-case distributional shift.
- Existing methods for robust policy learning consider the worst-case joint distribution of the covariate and the outcome, which can be unnecessarily conservative.
- This paper focuses on robust policy learning under concept drift, where only the conditional relationship between the outcome and the covariate changes.
- The paper proposes a learning algorithm that maximizes the estimated policy value within a given policy class, with an optimal sub-optimality gap.
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Arxiv
1w
297
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The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling
- A new approach called the Multiplex Classification Framework has been introduced to address the complexities of classification problems through problem transformation, ontology engineering, and model ensembling.
- The framework offers adaptability to any number of classes and logical constraints, a method for managing class imbalance, elimination of confidence threshold selection, and a modular structure.
- Experiments comparing the Multiplex approach with conventional classification models showed significant improvement in classification performance, especially in problems with a large number of classes and class imbalances.
- However, the Multiplex approach requires understanding of the problem domain and experience with ontology engineering, and involves training multiple models.
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Arxiv
1w
272
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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
- Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms are facing a performance gap when applied across different benchmark environments.
- While DMBRL algorithms perform well in OpenAI Gym, their performance drops significantly in DeepMind Control Suite (DMC) with proprioceptive observations.
- Modern techniques designed to address issues in these settings do not consistently improve performance across all environments.
- Adding synthetic rollouts to the training process, which is the backbone of Dyna-style algorithms, significantly degrades performance in most DMC environments.
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Arxiv
1w
125
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Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models
- This research proposes a training-free method for federated learning with pre-trained models.
- The method utilizes an unbiased estimator of class covariance matrices.
- It only requires the communication of class means, reducing communication costs.
- The approach improves performance by 4-26% compared to existing methods with the same communication cost.
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Arxiv
1w
310
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A Unifying Information-theoretic Perspective on Evaluating Generative Models
- There is significant current research focused on determining meaningful evaluation metrics for generative models.
- A unifying perspective is needed to allow for easier comparison and clearer explanation of metric benefits and drawbacks.
- A class of kth-nearest-neighbors (kNN)-based metrics is unified under an information-theoretic lens.
- A tri-dimensional metric composed of Precision Cross-Entropy (PCE), Recall Cross-Entropy (RCE), and Recall Entropy (RE) is proposed to measure fidelity and diversity.
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Arxiv
1w
159
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Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
- Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
- Realtime environments change as agents perform action inference and learning, requiring high interaction frequencies to minimize regret.
- Recent advances in machine learning involve larger neural networks with longer inference times, raising concerns about their applicability in realtime systems.
- Proposed algorithms for staggering asynchronous inference processes ensure consistent time intervals for actions, enabling use of models with high inference times.
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Arxiv
1w
37
Image Credit: Arxiv
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
- ResQ is a post-training quantization (PTQ) method for large language models (LLMs).
- ResQ uses principal component analysis (PCA) to identify a low-rank subspace with high activation variances.
- Within this subspace, ResQ keeps the coefficients in high precision while quantizing the rest to 4-bit.
- ResQ outperforms recent PTQ methods, achieving lower perplexity and faster inference on benchmarks.
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Arxiv
1w
192
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I0T: Embedding Standardization Method Towards Zero Modality Gap
- Contrastive Language-Image Pretraining (CLIP) enables zero-shot inference in downstream tasks such as image-text retrieval and classification.
- Recent works extending CLIP suffer from the issue of modality gap, which arises when the image and text embeddings are projected to disparate manifolds, deviating from the intended objective of image-text contrastive learning.
- Researchers propose two methods to address the modality gap: (1) a post-hoc embedding standardization method, I0T_post, that reduces the modality gap to zero and (2) a trainable method, I0T_async, that adds two normalization layers for each encoder to alleviate the modality gap.
- The I0T framework significantly reduces the modality gap while preserving the original embedding representations of trained models with their locked parameters and can serve as an alternative evaluation metric for CLIPScore.
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Arxiv
1w
184
Image Credit: Arxiv
Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning
- Continual learning in deep neural networks often suffers from catastrophic forgetting, where representations for previous tasks are overwritten during subsequent training.
- A novel sample retrieval strategy is proposed that leverages both gradient-conflicting and gradient-aligned samples to retain knowledge about past tasks.
- Gradient-conflicting samples are selected to reduce interference and re-align gradients, preserving past task knowledge.
- Experiments validate the method's state-of-the-art performance in mitigating forgetting and maintaining competitive accuracy on new tasks.
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Arxiv
1w
163
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Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
- Cherry-picking datasets in time series forecasting can significantly distort the perceived performance of forecasting methods.
- Selective dataset selection can lead to an exaggeration of the effectiveness of forecasting methods.
- By selectively choosing just four datasets, 46% of methods could be considered best in class.
- Increasing the number of datasets tested from 3 to 6 reduces the risk of incorrectly identifying an algorithm as the best one by approximately 40%.
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9 Likes
Arxiv
1w
339
Image Credit: Arxiv
CLDG: Contrastive Learning on Dynamic Graphs
- The paper introduces CLDG, a framework for contrastive learning on dynamic graphs.
- CLDG addresses the issue of changing semantics and labels in dynamic graphs during augmentation.
- It proposes a sampling layer to extract temporally-persistent signals and encourage consistent representations.
- CLDG outperforms state-of-the-art unsupervised methods on seven datasets and reduces model parameters and training time significantly.
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Arxiv
1w
180
Image Credit: Arxiv
HashAttention: Semantic Sparsity for Faster Inference
- HashAttention is a principled approach that efficiently identifies pivoted tokens for attention computation in AI systems.
- The approach uses Hamming space and bitwise operations to capture semantic similarity and reduce the number of tokens used.
- HashAttention can reduce the number of tokens used by a factor of 1/32 for the Llama-3.1-8B model with LongBench, while maintaining average quality.
- HashAttention is significantly faster than other existing methods on Nvidia-L4 GPU.
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