1,236 research outputs found
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
We formulate the problem of neural network optimization as Bayesian
filtering, where the observations are the backpropagated gradients. While
neural network optimization has previously been studied using natural gradient
methods which are closely related to Bayesian inference, they were unable to
recover standard optimizers such as Adam and RMSprop with a root-mean-square
gradient normalizer, instead getting a mean-square normalizer. To recover the
root-mean-square normalizer, we find it necessary to account for the temporal
dynamics of all the other parameters as they are geing optimized. The resulting
optimizer, AdaBayes, adaptively transitions between SGD-like and Adam-like
behaviour, automatically recovers AdamW, a state of the art variant of Adam
with decoupled weight decay, and has generalisation performance competitive
with SGD
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study
Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks
MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
Autonomous vehicles are expected to drive in complex scenarios with several
independent non cooperating agents. Path planning for safely navigating in such
environments can not just rely on perceiving present location and motion of
other agents. It requires instead to predict such variables in a far enough
future. In this paper we address the problem of multimodal trajectory
prediction exploiting a Memory Augmented Neural Network. Our method learns past
and future trajectory embeddings using recurrent neural networks and exploits
an associative external memory to store and retrieve such embeddings.
Trajectory prediction is then performed by decoding in-memory future encodings
conditioned with the observed past. We incorporate scene knowledge in the
decoding state by learning a CNN on top of semantic scene maps. Memory growth
is limited by learning a writing controller based on the predictive capability
of existing embeddings. We show that our method is able to natively perform
multi-modal trajectory prediction obtaining state-of-the art results on three
datasets. Moreover, thanks to the non-parametric nature of the memory module,
we show how once trained our system can continuously improve by ingesting novel
patterns.Comment: Accepted at CVPR2
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.Comment: NIPS 201
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