1,132 research outputs found
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
Unlike its image based counterpart, point cloud based retrieval for place
recognition has remained as an unexplored and unsolved problem. This is largely
due to the difficulty in extracting local feature descriptors from a point
cloud that can subsequently be encoded into a global descriptor for the
retrieval task. In this paper, we propose the PointNetVLAD where we leverage on
the recent success of deep networks to solve point cloud based retrieval for
place recognition. Specifically, our PointNetVLAD is a combination/modification
of the existing PointNet and NetVLAD, which allows end-to-end training and
inference to extract the global descriptor from a given 3D point cloud.
Furthermore, we propose the "lazy triplet and quadruplet" loss functions that
can achieve more discriminative and generalizable global descriptors to tackle
the retrieval task. We create benchmark datasets for point cloud based
retrieval for place recognition, and the experimental results on these datasets
show the feasibility of our PointNetVLAD. Our code and the link for the
benchmark dataset downloads are available in our project website.
http://github.com/mikacuy/pointnetvlad/Comment: CVPR 2018, 11 pages, 10 figure
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Inference in log-linear models scales linearly in the size of output space in
the worst-case. This is often a bottleneck in natural language processing and
computer vision tasks when the output space is feasibly enumerable but very
large. We propose a method to perform inference in log-linear models with
sublinear amortized cost. Our idea hinges on using Gumbel random variable
perturbations and a pre-computed Maximum Inner Product Search data structure to
access the most-likely elements in sublinear amortized time. Our method yields
provable runtime and accuracy guarantees. Further, we present empirical
experiments on ImageNet and Word Embeddings showing significant speedups for
sampling, inference, and learning in log-linear models.Comment: In UAI proceeding
Efficient, Noise-Tolerant, and Private Learning via Boosting
We introduce a simple framework for designing private boosting algorithms. We
give natural conditions under which these algorithms are differentially
private, efficient, and noise-tolerant PAC learners. To demonstrate our
framework, we use it to construct noise-tolerant and private PAC learners for
large-margin halfspaces whose sample complexity does not depend on the
dimension.
We give two sample complexity bounds for our large-margin halfspace learner.
One bound is based only on differential privacy, and uses this guarantee as an
asset for ensuring generalization. This first bound illustrates a general
methodology for obtaining PAC learners from privacy, which may be of
independent interest. The second bound uses standard techniques from the theory
of large-margin classification (the fat-shattering dimension) to match the best
known sample complexity for differentially private learning of large-margin
halfspaces, while additionally tolerating random label noise.Comment: 33 page
Ignorance-Aware Approaches and Algorithms for Prototype Selection in Machine Learning
Operating with ignorance is an important concern of the Machine Learning
research, especially when the objective is to discover knowledge from the
imperfect data. Data mining (driven by appropriate knowledge discovery tools)
is about processing available (observed, known and understood) samples of data
aiming to build a model (e.g., a classifier) to handle data samples, which are
not yet observed, known or understood. These tools traditionally take samples
of the available data (known facts) as an input for learning. We want to
challenge the indispensability of this approach and we suggest considering the
things the other way around. What if the task would be as follows: how to learn
a model based on our ignorance, i.e. by processing the shape of 'voids' within
the available data space? Can we improve traditional classification by modeling
also the ignorance? In this paper, we provide some algorithms for the discovery
and visualizing of the ignorance zones in two-dimensional data spaces and
design two ignorance-aware smart prototype selection techniques (incremental
and adversarial) to improve the performance of the nearest neighbor
classifiers. We present experiments with artificial and real datasets to test
the concept of the usefulness of ignorance discovery in machine learning
Linguistically-Informed Self-Attention for Semantic Role Labeling
Current state-of-the-art semantic role labeling (SRL) uses a deep neural
network with no explicit linguistic features. However, prior work has shown
that gold syntax trees can dramatically improve SRL decoding, suggesting the
possibility of increased accuracy from explicit modeling of syntax. In this
work, we present linguistically-informed self-attention (LISA): a neural
network model that combines multi-head self-attention with multi-task learning
across dependency parsing, part-of-speech tagging, predicate detection and SRL.
Unlike previous models which require significant pre-processing to prepare
linguistic features, LISA can incorporate syntax using merely raw tokens as
input, encoding the sequence only once to simultaneously perform parsing,
predicate detection and role labeling for all predicates. Syntax is
incorporated by training one attention head to attend to syntactic parents for
each token. Moreover, if a high-quality syntactic parse is already available,
it can be beneficially injected at test time without re-training our SRL model.
In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art
performance for a model using predicted predicates and standard word
embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art
on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in
error. On ConLL-2012 English SRL we also show an improvement of more than 2.5
F1. LISA also out-performs the state-of-the-art with contextually-encoded
(ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on
out-of-domain text.Comment: In Conference on Empirical Methods in Natural Language Processing
(EMNLP). Brussels, Belgium. October 201
Current Mathematical Methods Used in QSAR/QSPR Studies
This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future
Categorical Feature Compression via Submodular Optimization
In the era of big data, learning from categorical features with very large
vocabularies (e.g., 28 million for the Criteo click prediction dataset) has
become a practical challenge for machine learning researchers and
practitioners. We design a highly-scalable vocabulary compression algorithm
that seeks to maximize the mutual information between the compressed
categorical feature and the target binary labels and we furthermore show that
its solution is guaranteed to be within a factor of the
global optimal solution. To achieve this, we introduce a novel
re-parametrization of the mutual information objective, which we prove is
submodular, and design a data structure to query the submodular function in
amortized time (where is the input vocabulary size). Our
complete algorithm is shown to operate in time. Additionally, we
design a distributed implementation in which the query data structure is
decomposed across machines such that each machine only requires space, while still preserving the approximation guarantee and using only
logarithmic rounds of computation. We also provide analysis of simple
alternative heuristic compression methods to demonstrate they cannot achieve
any approximation guarantee. Using the large-scale Criteo learning task, we
demonstrate better performance in retaining mutual information and also verify
competitive learning performance compared to other baseline methods.Comment: Accepted to ICML 2019. Authors are listed in alphabetical orde
Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
Image features for retrieval-based localization must be invariant to dynamic
objects (e.g. cars) as well as seasonal and daytime changes. Such invariances
are, up to some extent, learnable with existing methods using triplet-like
losses, given a large number of diverse training images. However, due to the
high algorithmic training complexity, there exists insufficient comparison
between different loss functions on large datasets. In this paper, we train and
evaluate several localization methods on three different benchmark datasets,
including Oxford RobotCar with over one million images. This large scale
evaluation yields valuable insights into the generalizability and performance
of retrieval-based localization. Based on our findings, we develop a novel
method for learning more accurate and better generalizing localization
features. It consists of two main contributions: (i) a feature volume-based
loss function, and (ii) hard positive and pairwise negative mining. On the
challenging Oxford RobotCar night condition, our method outperforms the
well-known triplet loss by 24.4% in localization accuracy within 5m
Monotonic Calibrated Interpolated Look-Up Tables
Real-world machine learning applications may require functions that are
fast-to-evaluate and interpretable. In particular, guaranteed monotonicity of
the learned function can be critical to user trust. We propose meeting these
goals for low-dimensional machine learning problems by learning flexible,
monotonic functions using calibrated interpolated look-up tables. We extend the
structural risk minimization framework of lattice regression to train monotonic
look-up tables by solving a convex problem with appropriate linear inequality
constraints. In addition, we propose jointly learning interpretable
calibrations of each feature to normalize continuous features and handle
categorical or missing data, at the cost of making the objective non-convex. We
address large-scale learning through parallelization, mini-batching, and
propose random sampling of additive regularizer terms. Case studies with
real-world problems with five to sixteen features and thousands to millions of
training samples demonstrate the proposed monotonic functions can achieve
state-of-the-art accuracy on practical problems while providing greater
transparency to users.Comment: To appear (with minor revisions), Journal Machine Learning Research
201
Similarity Search and Locality Sensitive Hashing using TCAMs
Similarity search methods are widely used as kernels in various machine
learning applications. Nearest neighbor search (NNS) algorithms are often used
to retrieve similar entries, given a query. While there exist efficient
techniques for exact query lookup using hashing, similarity search using exact
nearest neighbors is known to be a hard problem and in high dimensions, best
known solutions offer little improvement over a linear scan. Fast solutions to
the approximate NNS problem include Locality Sensitive Hashing (LSH) based
techniques, which need storage polynomial in with exponent greater than
, and query time sublinear, but still polynomial in , where is the
size of the database. In this work we present a new technique of solving the
approximate NNS problem in Euclidean space using a Ternary Content Addressable
Memory (TCAM), which needs near linear space and has O(1) query time. In fact,
this method also works around the best known lower bounds in the cell probe
model for the query time using a data structure near linear in the size of the
data base. TCAMs are high performance associative memories widely used in
networking applications such as access control lists. A TCAM can query for a
bit vector within a database of ternary vectors, where every bit position
represents , or . The is a wild card representing either a or
a . We leverage TCAMs to design a variant of LSH, called Ternary Locality
Sensitive Hashing (TLSH) wherein we hash database entries represented by
vectors in the Euclidean space into . By using the added
functionality of a TLSH scheme with respect to the character, we solve an
instance of the approximate nearest neighbor problem with 1 TCAM access and
storage nearly linear in the size of the database. We believe that this work
can open new avenues in very high speed data mining.Comment: 11 pages, in SIGMOD 201
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