102 research outputs found
Microscopic and macroscopic approaches to the mental representations of second languages
With a particular reference to second language (L2), we discuss (1) how structural priming can be used to tap into L2 representations and their relationships with first and target language representations; and (2) how complex networks additionally can be used to reveal the global and local patterning of L2 linguistic features and L2 developmental trajectories
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
Invariant Feature Learning for Generalized Long-Tailed Classification
Existing long-tailed classification (LT) methods only focus on tackling the
class-wise imbalance that head classes have more samples than tail classes, but
overlook the attribute-wise imbalance. In fact, even if the class is balanced,
samples within each class may still be long-tailed due to the varying
attributes. Note that the latter is fundamentally more ubiquitous and
challenging than the former because attributes are not just implicit for most
datasets, but also combinatorially complex, thus prohibitively expensive to be
balanced. Therefore, we introduce a novel research problem: Generalized
Long-Tailed classification (GLT), to jointly consider both kinds of imbalances.
By "generalized", we mean that a GLT method should naturally solve the
traditional LT, but not vice versa. Not surprisingly, we find that most
class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT
and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment
of class distribution while neglecting to learn attribute-invariant features.
To this end, we propose an Invariant Feature Learning (IFL) method as the first
strong baseline for GLT. IFL first discovers environments with divergent
intra-class distributions from the imperfect predictions and then learns
invariant features across them. Promisingly, as an improved feature backbone,
IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and
ensemble. Codes and benchmarks are available on Github:
https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorchComment: Accepted to ECCV 2022. Codes and benchmarks are available on Github:
https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorc
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
Data augmentation has been established as an efficacious approach to
supplement useful information for low-resource datasets. Traditional
augmentation techniques such as noise injection and image transformations have
been widely used. In addition, generative data augmentation (GDA) has been
shown to produce more diverse and flexible data. While generative adversarial
networks (GANs) have been frequently used for GDA, they lack diversity and
controllability compared to text-to-image diffusion models. In this paper, we
propose TTIDA (Text-to-Text-to-Image Data Augmentation) to leverage the
capabilities of large-scale pre-trained Text-to-Text (T2T) and Text-to-Image
(T2I) generative models for data augmentation. By conditioning the T2I model on
detailed descriptions produced by T2T models, we are able to generate
photo-realistic labeled images in a flexible and controllable manner.
Experiments on in-domain classification, cross-domain classification, and image
captioning tasks show consistent improvements over other data augmentation
baselines. Analytical studies in varied settings, including few-shot,
long-tail, and adversarial, further reinforce the effectiveness of TTIDA in
enhancing performance and increasing robustness
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Human motion prediction from historical pose sequence is at the core of many
applications in machine intelligence. However, in current state-of-the-art
methods, the predicted future motion is confined within the same activity. One
can neither generate predictions that differ from the current activity, nor
manipulate the body parts to explore various future possibilities. Undoubtedly,
this greatly limits the usefulness and applicability of motion prediction. In
this paper, we propose a generalization of the human motion prediction task in
which control parameters can be readily incorporated to adjust the forecasted
motion. Our method is compelling in that it enables manipulable motion
prediction across activity types and allows customization of the human movement
in a variety of fine-grained ways. To this aim, a simple yet effective
composite GAN structure, consisting of local GANs for different body parts and
aggregated via a global GAN is presented. The local GANs game in lower
dimensions, while the global GAN adjusts in high dimensional space to avoid
mode collapse. Extensive experiments show that our method outperforms
state-of-the-art. The codes are available at
https://github.com/herolvkd/AM-GAN
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
With the popularity of cryptocurrencies and the remarkable development of
blockchain technology, decentralized applications emerged as a revolutionary
force for the Internet. Meanwhile, decentralized applications have also
attracted intense attention from the online gambling community, with more and
more decentralized gambling platforms created through the help of smart
contracts. Compared with conventional gambling platforms, decentralized
gambling have transparent rules and a low participation threshold, attracting a
substantial number of gamblers. In order to discover gambling behaviors and
identify the contracts and addresses involved in gambling, we propose a tool
termed ETHGamDet. The tool is able to automatically detect the smart contracts
and addresses involved in gambling by scrutinizing the smart contract code and
address transaction records. Interestingly, we present a novel LightGBM model
with memory components, which possesses the ability to learn from its own
misclassifications. As a side contribution, we construct and release a
large-scale gambling dataset at
https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future
research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and
0.89 in address classification and contract classification respectively, and
offers novel and interesting insights
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Road traffic forecasting plays a critical role in smart city initiatives and
has experienced significant advancements thanks to the power of deep learning
in capturing non-linear patterns of traffic data. However, the promising
results achieved on current public datasets may not be applicable to practical
scenarios due to limitations within these datasets. First, the limited sizes of
them may not reflect the real-world scale of traffic networks. Second, the
temporal coverage of these datasets is typically short, posing hurdles in
studying long-term patterns and acquiring sufficient samples for training deep
models. Third, these datasets often lack adequate metadata for sensors, which
compromises the reliability and interpretability of the data. To mitigate these
limitations, we introduce the LargeST benchmark dataset. It encompasses a total
number of 8,600 sensors in California with a 5-year time coverage and includes
comprehensive metadata. Using LargeST, we perform in-depth data analysis to
extract data insights, benchmark well-known baselines in terms of their
performance and efficiency, and identify challenges as well as opportunities
for future research. We release the datasets and baseline implementations at:
https://github.com/liuxu77/LargeST
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