8 accepted papers
机器人 快速掌握作技能是一项重大挑战， 受制于物理设备的寿命和安全 要求。目前，强化学习技术在 解决涉及丰富接触的动态、无结构问题 场景。然而，这些算法的收敛率通常为 由于机器人状态-动作映射的维度较高，速度较慢 空间以及广泛的初始政策搜索空间。与此同时， 大型语言模型（LLM）的进步赋予了这些模型 具有一定的逻辑推理能力，使他们能够接受 机器人初期阶段的主动目标导向行动 任务。这些模型可以隐式生成状态的特征和 揭示轨迹生成中的潜在模式。然而，复杂地说 涉及丰富接触场景的作性任务，LLM依然会失败 短暂。因此，整合了 的强大交互功能 &
With the rapid scaling of large language models, achieving efficient compression while maintaining model performance has become a critical challenge. To address the limitations of existing non-uniform quantization methods, which typically rely on fixed codebooks and require costly optimization, we p…
Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure time results in lots of noise in the blurry image. Although existing blind deblurring methods produce satisfactory resul…
In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence ra
Incomplete Multi-View Clustering (IMVC) has made significant progress by optimally merging multiple pre-specified incomplete views. Most existing IMVC algorithms operate under the assumption that view alignment is known, but in practice, the coupling information between views may be absent, thereby…
Incomplete Multiple Kernel Clustering algorithms, which aim to learn a common latent representation from pre-constructed incomplete multiple kernels from the original data, followed by k-means for clustering. They have attracted intensive attention due to their high computational efficiency. However…
Large-scale datasets in the real world inevitably involve label noise. Deep models can gradually overfit noisy labels and thus degrade model generalization. To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance. Due…
Noisy labels are ubiquitous in natural language processing (NLP) tasks. Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used. To address this issue, SGD noise has been explored to provide a more…