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Found 12 papers, 5 papers with code
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience.
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
To address problems such as lack of relevant knowledge and poor control over subtasks of varying importance, we propose a Hierarchical Expert Prompt (HEP) for LLM.
Decoding Interpretable Logic Rules from Neural Networks
Our empirical study demonstrates that NeuroLogic can extract global and interpretable rules from state-of-the-art models such as ResNet, a task at which existing work struggles.
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge.
LLM-PySC2: Starcraft II learning environment for Large Language Models
This paper introduces a new environment LLM-PySC2 (the Large Language Model StarCraft II Learning Environment), a platform derived from DeepMind's StarCraft II Learning Environment that serves to develop Large Language Models (LLMs) based decision-making methodologies.
From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model
Subsequently, the Street Image Generation phase utilizes these segmentations as a condition to guide a fine-tuned latent diffusion model.
Momentum Auxiliary Network for Supervised Local Learning
Supervised local learning, which segments the network into multiple local blocks updated by independent auxiliary networks.
HPFF: Hierarchical Locally Supervised Learning with Patch Feature Fusion
To overcome these limitations, we propose a novel model called HPFF that performs hierarchical locally supervised learning and patch-level feature computation on the auxiliary networks.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Monocular 3D object detection plays a crucial role in autonomous driving.
Scalar Invariant Networks with Zero Bias
However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles.
Towards Reliable Neural Specifications
We show that by using NAP, we can verify a significant region of the input space, while still recalling 84% of the data on MNIST.
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
