327 research outputs found
Low-Rank Modular Reinforcement Learning via Muscle Synergy
Modular Reinforcement Learning (RL) decentralizes the control of multi-joint
robots by learning policies for each actuator. Previous work on modular RL has
proven its ability to control morphologically different agents with a shared
actuator policy. However, with the increase in the Degree of Freedom (DoF) of
robots, training a morphology-generalizable modular controller becomes
exponentially difficult. Motivated by the way the human central nervous system
controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR)
framework that exploits the redundant nature of DoF in robot control. Actuators
are grouped into synergies by an unsupervised learning method, and a synergy
action is learned to control multiple actuators in synchrony. In this way, we
achieve a low-rank control at the synergy level. We extensively evaluate our
method on a variety of robot morphologies, and the results show its superior
efficiency and generalizability, especially on robots with a large DoF like
Humanoids++ and UNIMALs.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022
Knowledge orchestration and digital innovation networks: insights from the Chinese context
As digital innovation increasingly pushes heterogeneous actors to connect with each other across multiple organizational and community boundaries, a doubly distributed innovation network may emerge, leading to the knowledge being too fragmented and heterogeneous. Facing this problem, I place an emphasis on material artefacts and social network structures in the cultural context of Chinese digital innovators. On the one hand, as innovation is increasingly mediated by material artefacts, I focus on epistemic objects and activity objects, which are able to motivate the process of innovation. On the other hand, as innovation transforms the network actors’ social space, I focus on the role of “guanxi” (i.e. a system of influential relationships in Chinese culture) and structural holes (i.e. the absence of a connection between two contacts) in digital innovation networks. At the same time, as the literature recognizes knowledge orchestration as a useful starting point to address the knowledge fragmentation and heterogeneity, I identify five activities as knowledge orchestration: knowledge mobilization, knowledge coordination, knowledge sharing, knowledge acquisition and knowledge integration. As traditional tools used to support knowledge management can no longer handle the fragmented and heterogeneous knowledge, there is limited studies contributing to our understanding of how the Chinese innovators use objects and social network structures to orchestrate knowledge in their innovation networks.
With these paucities of research in mind, this thesis explores how the material objects and the social network structures orchestrate knowledge for coordinating the fragmented and heterogeneous knowledge in Chinese digital innovation networks. From the perspective of material artefacts, my first study explores how epistemic objects affect the acquisition, integration and sharing of knowledge among collaborative organizations during their IT innovation alliances. My second study explores how activity objects affect the sharing, acquisition and integration of knowledge for crowdsourced digital innovation. From a social perspective, my third study explores how guanxi and structural holes affect the mobilization and coordination of knowledge among Chinese digital entrepreneurs in their innovation networks. Following the three studies, I show my key contributions, and discuss my theoretical and practical implications
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
We analytically investigate how over-parameterization of models in randomized
machine learning algorithms impacts the information leakage about their
training data. Specifically, we prove a privacy bound for the KL divergence
between model distributions on worst-case neighboring datasets, and explore its
dependence on the initialization, width, and depth of fully connected neural
networks. We find that this KL privacy bound is largely determined by the
expected squared gradient norm relative to model parameters during training.
Notably, for the special setting of linearized network, our analysis indicates
that the squared gradient norm (and therefore the escalation of privacy loss)
is tied directly to the per-layer variance of the initialization distribution.
By using this analysis, we demonstrate that privacy bound improves with
increasing depth under certain initializations (LeCun and Xavier), while
degrades with increasing depth under other initializations (He and NTK). Our
work reveals a complex interplay between privacy and depth that depends on the
chosen initialization distribution. We further prove excess empirical risk
bounds under a fixed KL privacy budget, and show that the interplay between
privacy utility trade-off and depth is similarly affected by the
initialization
Empowering Graph Representation Learning with Test-Time Graph Transformation
As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have facilitated various applications from drug discovery to
recommender systems. Nevertheless, the effectiveness of GNNs is immensely
challenged by issues related to data quality, such as distribution shift,
abnormal features and adversarial attacks. Recent efforts have been made on
tackling these issues from a modeling perspective which requires additional
cost of changing model architectures or re-training model parameters. In this
work, we provide a data-centric view to tackle these issues and propose a graph
transformation framework named GTrans which adapts and refines graph data at
test time to achieve better performance. We provide theoretical analysis on the
design of the framework and discuss why adapting graph data works better than
adapting the model. Extensive experiments have demonstrated the effectiveness
of GTrans on three distinct scenarios for eight benchmark datasets where
suboptimal data is presented. Remarkably, GTrans performs the best in most
cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on
three experimental settings
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the
possibility of acquiring multiple omics data from individual cells, thereby
enabling a deeper understanding of cellular states and dynamics. Nevertheless,
the proliferation of multimodal single-cell data also introduces tremendous
challenges in modeling the complex interactions among different modalities. The
recently advanced methods focus on constructing static interaction graphs and
applying graph neural networks (GNNs) to learn from multimodal data. However,
such static graphs can be suboptimal as they do not take advantage of the
downstream task information; meanwhile GNNs also have some inherent limitations
when deeply stacking GNN layers. To tackle these issues, in this work, we
investigate how to leverage transformers for multimodal single-cell data in an
end-to-end manner while exploiting downstream task information. In particular,
we propose a scMoFormer framework which can readily incorporate external domain
knowledge and model the interactions within each modality and cross modalities.
Extensive experiments demonstrate that scMoFormer achieves superior performance
on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal
with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022
competition. Our implementation is publicly available at Github.Comment: CIKM 202
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations
Recent advancements in implicit neural representations have contributed to
high-fidelity surface reconstruction and photorealistic novel view synthesis.
However, the computational complexity inherent in these methodologies presents
a substantial impediment, constraining the attainable frame rates and
resolutions in practical applications. In response to this predicament, we
propose VQ-NeRF, an effective and efficient pipeline for enhancing implicit
neural representations via vector quantization. The essence of our method
involves reducing the sampling space of NeRF to a lower resolution and
subsequently reinstating it to the original size utilizing a pre-trained VAE
decoder, thereby effectively mitigating the sampling time bottleneck
encountered during rendering. Although the codebook furnishes representative
features, reconstructing fine texture details of the scene remains challenging
due to high compression rates. To overcome this constraint, we design an
innovative multi-scale NeRF sampling scheme that concurrently optimizes the
NeRF model at both compressed and original scales to enhance the network's
ability to preserve fine details. Furthermore, we incorporate a semantic loss
function to improve the geometric fidelity and semantic coherence of our 3D
reconstructions. Extensive experiments demonstrate the effectiveness of our
model in achieving the optimal trade-off between rendering quality and
efficiency. Evaluation on the DTU, BlendMVS, and H3DS datasets confirms the
superior performance of our approach.Comment: Submitted to the 38th Annual AAAI Conference on Artificial
Intelligenc
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