Search Results for author:
Found 8 papers, 2 papers with code
MoST: Multi-modality Scene Tokenization for Motion Prediction
This symbolic representation is a high-level abstraction of the real world, which may render the motion prediction model vulnerable to perception errors (e. g., failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e. g., poor road conditions).
Scaling Motion Forecasting Models with Ensemble Distillation
These experiments demonstrate distillation from ensembles as an effective method for improving accuracy of predictive models for robotic systems with limited compute budgets.
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous.
Ranked #6 on
Motion Forecasting
on Argoverse CVPR 2020
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Different from the conventional LSTM, we share the information between multiple LSTMs through a new pooling layer.
Ranked #1 on
Trajectory Prediction
on Stanford Drone
(FDE(8/12) @K=5 metric)
A Recurrent Latent Variable Model for Sequential Data
In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder.
Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network.
A Novel Feature Selection and Extraction Technique for Classification
This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs).
Learning Temporal Dependencies in Data Using a DBN-BLSTM
Since the advent of deep learning, it has been used to solve various problems using many different architectures.
