131 research outputs found
Efficient Online Decision Tree Learning with Active Feature Acquisition
Constructing decision trees online is a classical machine learning problem.
Existing works often assume that features are readily available for each
incoming data point. However, in many real world applications, both feature
values and the labels are unknown a priori and can only be obtained at a cost.
For example, in medical diagnosis, doctors have to choose which tests to
perform (i.e., making costly feature queries) on a patient in order to make a
diagnosis decision (i.e., predicting labels). We provide a fresh perspective to
tackle this practical challenge. Our framework consists of an active planning
oracle embedded in an online learning scheme for which we investigate several
information acquisition functions. Specifically, we employ a surrogate
information acquisition function based on adaptive submodularity to actively
query feature values with a minimal cost, while using a posterior sampling
scheme to maintain a low regret for online prediction. We demonstrate the
efficiency and effectiveness of our framework via extensive experiments on
various real-world datasets. Our framework also naturally adapts to the
challenging setting of online learning with concept drift and is shown to be
competitive with baseline models while being more flexible
From biomaterial-based data storage to bio-inspired artificial synapse
The implementation of biocompatible and biodegradable information storage would be a significant step toward next-generation green electronics. On the other hand, benefiting from high density, multifunction, low power consumption and multilevel data storage, artificial synapses exhibit attractive future for built-in nonvolatile memories and reconstructed logic operations. Here, we provide a comprehensive and critical review on the developments of bio-memories with a view to inspire more intriguing ideas on this area that may finally open up a new chapter in next-generation consumer electronics. We will discuss that biomolecule-based memory employed evolutionary natural biomaterials as data storage node and artificial synapse emulated biological synapse function, which is expected to conquer the bottleneck of the traditional von Neumann architecture. Finally, challenges and opportunities in the aforementioned bio-memory area are presented
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Standard contextual bandit problem assumes that all the relevant contexts are
observed before the algorithm chooses an arm. This modeling paradigm, while
useful, often falls short when dealing with problems in which valuable
additional context can be observed after arm selection. For example, content
recommendation platforms like Youtube, Instagram, Tiktok also observe valuable
follow-up information pertinent to the user's reward after recommendation
(e.g., how long the user stayed, what is the user's watch speed, etc.). To
improve online learning efficiency in these applications, we study a novel
contextual bandit problem with post-serving contexts and design a new
algorithm, poLinUCB, that achieves tight regret under standard assumptions.
Core to our technical proof is a robustified and generalized version of the
well-known Elliptical Potential Lemma (EPL), which can accommodate noise in
data. Such robustification is necessary for tackling our problem, and we
believe it could also be of general interest. Extensive empirical tests on both
synthetic and real-world datasets demonstrate the significant benefit of
utilizing post-serving contexts as well as the superior performance of our
algorithm over the state-of-the-art approaches.Comment: NeurIPS 2023 (Spotlight
Development of Landmark-based Facial Asymmetry Evaluation
This paper presents a novel development of a real-time evaluation system for facial asymmetry. Using this tool, it is expected possibly to assess the severity of facial asymmetry based on local and global comparisons of facial components. While the local one measures distances between the landmarks inside each facial region, the global one focuses on the overall difference of all facial regions about the sagittal plane. This reported preliminary work is focused on assessing the suitability of the existing image landmark detection methods for a real-time evaluation. Three commonly used deep learning-based methodologies have been implemented and tested in order to identify robust facial landmark detection under various challenging conditions. It is hypothesized, that the proposed system will be able to provide a more accurate measurement of facial asymmetry. The key is the proposed use of the geodesic distance calculated based on the geometry of human faces, with the help of the state-of-art depth camera
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
With the rapid development of 3D vision, point cloud has become an
increasingly popular 3D visual media content. Due to the irregular structure,
point cloud has posed novel challenges to the related research, such as
compression, transmission, rendering and quality assessment. In these latest
researches, point cloud quality assessment (PCQA) has attracted wide attention
due to its significant role in guiding practical applications, especially in
many cases where the reference point cloud is unavailable. However, current
no-reference metrics which based on prevalent deep neural network have apparent
disadvantages. For example, to adapt to the irregular structure of point cloud,
they require preprocessing such as voxelization and projection that introduce
extra distortions, and the applied grid-kernel networks, such as Convolutional
Neural Networks, fail to extract effective distortion-related features.
Besides, they rarely consider the various distortion patterns and the
philosophy that PCQA should exhibit shifting, scaling, and rotational
invariance. In this paper, we propose a novel no-reference PCQA metric named
the Graph convolutional PCQA network (GPA-Net). To extract effective features
for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which
attentively captures the perturbation of structure and texture. Then, we
propose the multi-task framework consisting of one main task (quality
regression) and two auxiliary tasks (distortion type and degree predictions).
Finally, we propose a coordinate normalization module to stabilize the results
of GPAConv under shift, scale and rotation transformations. Experimental
results on two independent databases show that GPA-Net achieves the best
performance compared to the state-of-the-art no-reference PCQA metrics, even
better than some full-reference metrics in some cases
Towards provably efficient quantum algorithms for large-scale machine-learning models
Large machine learning models are revolutionary technologies of artificial
intelligence whose bottlenecks include huge computational expenses, power, and
time used both in the pre-training and fine-tuning process. In this work, we
show that fault-tolerant quantum computing could possibly provide provably
efficient resolutions for generic (stochastic) gradient descent algorithms,
scaling as , where is the size
of the models and is the number of iterations in the training, as long as
the models are both sufficiently dissipative and sparse, with small learning
rates. Based on earlier efficient quantum algorithms for dissipative
differential equations, we find and prove that similar algorithms work for
(stochastic) gradient descent, the primary algorithm for machine learning. In
practice, we benchmark instances of large machine learning models from 7
million to 103 million parameters. We find that, in the context of sparse
training, a quantum enhancement is possible at the early stage of learning
after model pruning, motivating a sparse parameter download and re-upload
scheme. Our work shows solidly that fault-tolerant quantum algorithms could
potentially contribute to most state-of-the-art, large-scale machine-learning
problems.Comment: 7+30 pages, 3+5 figure
Towards provably efficient quantum algorithms for large-scale machine-learning models
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 x polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems
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Cavitation in soft matter
Cavitation is the sudden, unstable expansion of a void or bubble within a liquid or solid subjected to a negative hydrostatic stress. Cavitation rheology is a field emerging from the development of a suite of materials characterization, damage quantification, and therapeutic techniques that exploit the physical principles of cavitation. Cavitation rheology is inherently complex and broad in scope with wide-ranging applications in the biology, chemistry, materials, and mechanics communities. This perspective aims to drive collaboration among these communities and guide discussion by defining a common core of high-priority goals while highlighting emerging opportunities in the field of cavitation rheology. A brief overview of the mechanics and dynamics of cavitation in soft matter is presented. This overview is followed by a discussion of the overarching goals of cavitation rheology and an overview of common experimental techniques. The larger unmet needs and challenges of cavitation in soft matter are then presented alongside specific opportunities for researchers from different disciplines to contribute to the field
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in neural video
compression (NVC). Due to the frame reference structure, current NVC methods
using the same R-D (Rate-Distortion) trade-off parameter for all
frames are suboptimal, which brings the need for bit allocation. Unlike
previous methods based on heuristic and empirical R-D models, we propose to
solve this problem by gradient-based optimization. Specifically, we first
propose a continuous bit implementation method based on Semi-Amortized
Variational Inference (SAVI). Then, we propose a pixel-level implicit bit
allocation method using iterative optimization by changing the SAVI target.
Moreover, we derive the precise R-D model based on the differentiable trait of
NVC. And we show the optimality of our method by proofing its equivalence to
the bit allocation with precise R-D model. Experimental results show that our
approach significantly improves NVC methods and outperforms existing bit
allocation methods. Our approach is plug-and-play for all differentiable NVC
methods, and it can be directly adopted on existing pre-trained models
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