212 research outputs found
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learning -
the options framework - with deep Q-networks (DQNs) through the use of
different "option heads" on the policy network, and a supervisory network for
choosing between the different options. We utilise our setup to investigate the
effects of architectural constraints in subtasks with positive and negative
transfer, across a range of network capacities. We empirically show that our
augmented DQN has lower sample complexity when simultaneously learning subtasks
with negative transfer, without degrading performance when learning subtasks
with positive transfer.Comment: IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and
Challenge
Functional Knowledge Transfer with Self-supervised Representation Learning
This work investigates the unexplored usability of self-supervised
representation learning in the direction of functional knowledge transfer. In
this work, functional knowledge transfer is achieved by joint optimization of
self-supervised learning pseudo task and supervised learning task, improving
supervised learning task performance. Recent progress in self-supervised
learning uses a large volume of data, which becomes a constraint for its
applications on small-scale datasets. This work shares a simple yet effective
joint training framework that reinforces human-supervised task learning by
learning self-supervised representations just-in-time and vice versa.
Experiments on three public datasets from different visual domains, Intel
Image, CIFAR, and APTOS, reveal a consistent track of performance improvements
on classification tasks during joint optimization. Qualitative analysis also
supports the robustness of learnt representations. Source code and trained
models are available on GitHub.Comment: Accepted at IEEE International Conference on Image Processing (ICIP
2023
Deep Polyphonic ADSR Piano Note Transcription
We investigate a late-fusion approach to piano transcription, combined with a
strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM).
The network architecture under consideration is compact in terms of its number
of parameters and easy to train with gradient descent. The network outputs are
fused over time in the final stage to obtain note segmentations, with an HMM
whose transition probabilities are chosen based on a model of attack, decay,
sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note
segments are then subject to a final binary decision rule to reject too weak
note segment hypotheses. We obtain state-of-the-art results on the MAPS
dataset, and are able to outperform other approaches by a large margin, when
predicting complete note regions from onsets to offsets.Comment: 5 pages, 2 figures, published as ICASSP'1
Deep Convolutional Neural Networks for MultilabelPrediction Using RGBD Data
Robotics relies heavily on the system's ability to perceive the world around the robot accurately and quickly. In a narrow setting as in manufacturing this goal is relatively simple. To make robotics feasible in more dynamic settings we must handle more objects, more attributes, and events that may be out of the scope of what a system has been exposed to previously. To this end, the present work focuses on automatic feature formation from RGB-D data, using deep convolutional neural networks, in order to recognize, not only objects but also attributes which are more applicable across objects, including those objects which have not been seen previously. Progress is shown in relation to more standard systems and near real-time classification of multiple targets is achieved
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more
attention. Training data extraction is therefore of great importance, as it can
serve as a potential tool to assess privacy leakage. However, due to the
difficulty of this task, most of the existing methods are proof-of-concept and
still not effective enough. In this paper, we investigate and benchmark tricks
for improving training data extraction using a publicly available dataset.
Because most existing extraction methods use a pipeline of
generating-then-ranking, i.e., generating text candidates as potential training
data and then ranking them based on specific criteria, our research focuses on
the tricks for both text generation (e.g., sampling strategy) and text ranking
(e.g., token-level criteria). The experimental results show that several
previously overlooked tricks can be crucial to the success of training data
extraction. Based on the GPT-Neo 1.3B evaluation results, our proposed tricks
outperform the baseline by a large margin in most cases, providing a much
stronger baseline for future research. The code is available at
https://github.com/weichen-yu/LM-Extraction.Comment: ICML 202
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