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Found 14 papers, 6 papers with code
A Case for Validation Buffer in Pessimistic Actor-Critic
VPL uses a small validation buffer to adjust the levels of pessimism throughout the agent training, with the pessimism set such that the approximation error of the critic targets is minimized.
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
We tested these agents across 14 diverse tasks from 2 simulation benchmarks.
Scaling Laws for Fine-Grained Mixture of Experts
Our findings not only show that MoE models consistently outperform dense Transformers but also highlight that the efficiency gap between dense and MoE models widens as we scale up the model size and training budget.
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers.
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics
Risk-aware Reinforcement Learning (RL) algorithms like SAC and TD3 were shown empirically to outperform their risk-neutral counterparts in a variety of continuous-action tasks.
Mixture of Tokens: Efficient LLMs through Cross-Example Aggregation
The operation of matching experts and tokens is discrete, which makes MoE models prone to issues like training instability and uneven expert utilization.
Grasping Student: semi-supervised learning for robotic manipulation
Gathering real-world data from the robot quickly becomes a bottleneck when constructing a robot learning system for grasping.
Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding
We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation.
On Many-Actions Policy Gradient
We study the variance of stochastic policy gradients (SPGs) with many action samples per state.
One Simple Trick to Fix Your Bayesian Neural Network
One of the most popular estimation methods in Bayesian neural networks (BNN) is mean-field variational inference (MFVI).
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
We present n-CPS - a generalisation of the recent state-of-the-art cross pseudo supervision (CPS) approach for the task of semi-supervised semantic segmentation.
Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps
In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2. 0 dataset.
Approximation and Parameterized Complexity of Minimax Approval Voting
Motivated by this, we then show a parameterized approximation scheme, running in time $\mathcal{O}^\star(\left({3}/{\epsilon}\right)^{2d})$, which is essentially tight assuming ETH.
Solving weighted and counting variants of connectivity problems parameterized by treewidth deterministically in single exponential time
It is well known that many local graph problems, like Vertex Cover and Dominating Set, can be solved in 2^{O(tw)}|V|^{O(1)} time for graphs G=(V, E) with a given tree decomposition of width tw.
Data Structures and Algorithms Computational Complexity Discrete Mathematics F.2.2; G.2.8
