40,061 research outputs found
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
Safe Exploration for Optimization with Gaussian Processes
We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
Complete and safe resection of challenging retroperitoneal tumors: anticipation of multi-organ and major vascular resection and use of adjunct procedures.
BackgroundRetroperitoneal tumors are often massive and can involve adjacent organs and/or vital structures, making them difficult to resect. Completeness of resection is within the surgeon's control and critical for long-term survival, particularly for malignant disease. Few studies directly address strategies for complete and safe resection of challenging retroperitoneal tumors.MethodsFifty-six patients representing 63 cases of primary or recurrent retroperitoneal tumor resection between 2004-2009 were identified and a retrospective chart review was performed. Rates of complete resection, use of adjunct procedures, and perioperative complications were recorded.ResultsIn 95% of cases, complete resection was achieved. Fifty-eight percent of these cases required en bloc multi-organ resection, and 8% required major vascular resection. Complete resection rates were higher for primary versus recurrent disease. Adjunct procedures (ureteral stents, femoral nerve monitoring, posterior laminotomy, etc.) were used in 54% of cases. Major postoperative complications occurred in 16% of cases, and one patient died (2% mortality).ConclusionsComplete resection of challenging retroperitoneal tumors is feasible and can be done safely with important pre- and intraoperative considerations in mind
Causally Regularized Learning with Agnostic Data Selection Bias
Most of previous machine learning algorithms are proposed based on the i.i.d.
hypothesis. However, this ideal assumption is often violated in real
applications, where selection bias may arise between training and testing
process. Moreover, in many scenarios, the testing data is not even available
during the training process, which makes the traditional methods like transfer
learning infeasible due to their need on prior of test distribution. Therefore,
how to address the agnostic selection bias for robust model learning is of
paramount importance for both academic research and real applications. In this
paper, under the assumption that causal relationships among variables are
robust across domains, we incorporate causal technique into predictive modeling
and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
by jointly optimize global confounder balancing and weighted logistic
regression. Global confounder balancing helps to identify causal features,
whose causal effect on outcome are stable across domains, then performing
logistic regression on those causal features constructs a robust predictive
model against the agnostic bias. To validate the effectiveness of our CRLR
algorithm, we conduct comprehensive experiments on both synthetic and real
world datasets. Experimental results clearly demonstrate that our CRLR
algorithm outperforms the state-of-the-art methods, and the interpretability of
our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18
Semantic Pose using Deep Networks Trained on Synthetic RGB-D
In this work we address the problem of indoor scene understanding from RGB-D
images. Specifically, we propose to find instances of common furniture classes,
their spatial extent, and their pose with respect to generalized class models.
To accomplish this, we use a deep, wide, multi-output convolutional neural
network (CNN) that predicts class, pose, and location of possible objects
simultaneously. To overcome the lack of large annotated RGB-D training sets
(especially those with pose), we use an on-the-fly rendering pipeline that
generates realistic cluttered room scenes in parallel to training. We then
perform transfer learning on the relatively small amount of publicly available
annotated RGB-D data, and find that our model is able to successfully annotate
even highly challenging real scenes. Importantly, our trained network is able
to understand noisy and sparse observations of highly cluttered scenes with a
remarkable degree of accuracy, inferring class and pose from a very limited set
of cues. Additionally, our neural network is only moderately deep and computes
class, pose and position in tandem, so the overall run-time is significantly
faster than existing methods, estimating all output parameters simultaneously
in parallel on a GPU in seconds.Comment: ICCV 2015 Submissio
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