14 research outputs found
Large-Margin Determinantal Point Processes
Determinantal point processes (DPPs) offer a powerful approach to modeling
diversity in many applications where the goal is to select a diverse subset. We
study the problem of learning the parameters (the kernel matrix) of a DPP from
labeled training data. We make two contributions. First, we show how to
reparameterize a DPP's kernel matrix with multiple kernel functions, thus
enhancing modeling flexibility. Second, we propose a novel parameter estimation
technique based on the principle of large margin separation. In contrast to the
state-of-the-art method of maximum likelihood estimation, our large-margin loss
function explicitly models errors in selecting the target subsets, and it can
be customized to trade off different types of errors (precision vs. recall).
Extensive empirical studies validate our contributions, including applications
on challenging document and video summarization, where flexibility in modeling
the kernel matrix and balancing different errors is indispensable.Comment: 15 page
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-suited
for modeling repulsion. In machine learning, the focus of DPP-based models has
been on diverse subset selection from a discrete and finite base set. This
discrete setting admits an efficient sampling algorithm based on the
eigendecomposition of the defining kernel matrix. Recently, there has been
growing interest in using DPPs defined on continuous spaces. While the
discrete-DPP sampler extends formally to the continuous case, computationally,
the steps required are not tractable in general. In this paper, we present two
efficient DPP sampling schemes that apply to a wide range of kernel functions:
one based on low rank approximations via Nystrom and random Fourier feature
techniques and another based on Gibbs sampling. We demonstrate the utility of
continuous DPPs in repulsive mixture modeling and synthesizing human poses
spanning activity spaces
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
The large volume of video content and high viewing frequency demand automatic
video summarization algorithms, of which a key property is the capability of
modeling diversity. If videos are lengthy like hours-long egocentric videos, it
is necessary to track the temporal structures of the videos and enforce local
diversity. The local diversity refers to that the shots selected from a short
time duration are diverse but visually similar shots are allowed to co-exist in
the summary if they appear far apart in the video. In this paper, we propose a
novel probabilistic model, built upon SeqDPP, to dynamically control the time
span of a video segment upon which the local diversity is imposed. In
particular, we enable SeqDPP to learn to automatically infer how local the
local diversity is supposed to be from the input video. The resulting model is
extremely involved to train by the hallmark maximum likelihood estimation
(MLE), which further suffers from the exposure bias and non-differentiable
evaluation metrics. To tackle these problems, we instead devise a reinforcement
learning algorithm for training the proposed model. Extensive experiments
verify the advantages of our model and the new learning algorithm over
MLE-based methods
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Graph convolutional networks (GCNs) have achieved great success in graph
representation learning by extracting high-level features from nodes and their
topology. Since GCNs generally follow a message-passing mechanism, each node
aggregates information from its first-order neighbour to update its
representation. As a result, the representations of nodes with edges between
them should be positively correlated and thus can be considered positive
samples. However, there are more non-neighbour nodes in the whole graph, which
provide diverse and useful information for the representation update. Two
non-adjacent nodes usually have different representations, which can be seen as
negative samples. Besides the node representations, the structural information
of the graph is also crucial for learning. In this paper, we used
quality-diversity decomposition in determinant point processes (DPP) to obtain
diverse negative samples. When defining a distribution on diverse subsets of
all non-neighbouring nodes, we incorporate both graph structure information and
node representations. Since the DPP sampling process requires matrix eigenvalue
decomposition, we propose a new shortest-path-base method to improve
computational efficiency. Finally, we incorporate the obtained negative samples
into the graph convolution operation. The ideas are evaluated empirically in
experiments on node classification tasks. These experiments show that the newly
proposed methods not only improve the overall performance of standard
representation learning but also significantly alleviate over-smoothing
problems.Comment: Accepted by IEEE TNNLS on 30-Aug-2023. arXiv admin note: text overlap
with arXiv:2210.0072
Probabilistic Typology: Deep Generative Models of Vowel Inventories
Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For ex- ample, all languages have vowels, while most—but not all—languages have an [u] sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a se- ries of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages