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Found 18 papers, 5 papers with code
EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning
This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in humanlike non-verbal communication.
Local Policies Enable Zero-shot Long-horizon Manipulation
We transfer our local policies from simulation to reality and observe they can solve unseen long-horizon manipulation tasks with up to 8 stages with significant pose, object and scene configuration variation.
On the benefits of pixel-based hierarchical policies for task generalization
Reinforcement learning practitioners often avoid hierarchical policies, especially in image-based observation spaces.
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
This paper focuses on decision planning with uncertainty estimation to address the hallucination problem in language models.
Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks.
Value function estimation using conditional diffusion models for control
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data.
GAUDI: A Neural Architect for Immersive 3D Scene Generation
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
Position Prediction as an Effective Pretraining Strategy
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec models in Speech and, recently, in MAE models in Vision, forces the model to learn about relationships between the content in different parts of the input using autoencoding related objectives.
Efficient Representation Learning via Adaptive Context Pooling
Inspired by the success of ConvNets that are combined with pooling to capture long-range dependencies, we learn to pool neighboring features for each token before computing attention in a given attention layer.
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation
Learning generalizeable policies from visual input in the presence of visual distractions is a challenging problem in reinforcement learning.
Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models
Modeling the world can benefit robot learning by providing a rich training signal for shaping an agent's latent state space.
A Dot Product Attention Free Transformer
We introduce Dot Product Attention Free Transformer (DAFT), an efficient variant of Transformers \citep{transformer} that eliminates the query-key dot product in self attention.
Ranked #679 on
Image Classification
on ImageNet
Regularized Training of Nearest Neighbor Language Models
In particular, we find that the added L2 regularization seems to improve the performance for high-frequency words without deteriorating the performance for low frequency ones.
An Attention Free Transformer
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention.
On the generalization of learning-based 3D reconstruction
State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training.
Set Distribution Networks: a Generative Model for Sets of Images
We introduce Set Distribution Networks (SDNs), a novel framework that learns to autoencode and freely generate sets.
Adversarial Fisher Vectors for Unsupervised Representation Learning
In contrast to a traditional view where the discriminator learns a constant function when reaching convergence, here we show that it can provide useful information for downstream tasks, e. g., feature extraction for classification.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric.
