715 research outputs found
Understanding Data Augmentation from a Robustness Perspective
In the realm of visual recognition, data augmentation stands out as a pivotal
technique to amplify model robustness. Yet, a considerable number of existing
methodologies lean heavily on heuristic foundations, rendering their intrinsic
mechanisms ambiguous. This manuscript takes both a theoretical and empirical
approach to understanding the phenomenon. Theoretically, we frame the discourse
around data augmentation within game theory's constructs. Venturing deeper, our
empirical evaluations dissect the intricate mechanisms of emblematic data
augmentation strategies, illuminating that these techniques primarily stimulate
mid- and high-order game interactions. Beyond the foundational exploration, our
experiments span multiple datasets and diverse augmentation techniques,
underscoring the universal applicability of our findings. Recognizing the vast
array of robustness metrics with intricate correlations, we unveil a
streamlined proxy. This proxy not only simplifies robustness assessment but
also offers invaluable insights, shedding light on the inherent dynamics of
model game interactions and their relation to overarching system robustness.
These insights provide a novel lens through which we can re-evaluate model
safety and robustness in visual recognition tasks.Comment: Not published yet. arXiv admin note: text overlap with
arXiv:2212.0405
GW25-e0067 Visit-to-visit variability of systolic blood pressure correlated with arterial stiffness in pre-hypertension
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Representation Learning on Knowledge Graphs (KGs) is essential for downstream
tasks. The dominant approach, KG Embedding (KGE), represents entities with
independent vectors and faces the scalability challenge. Recent studies propose
an alternative way for parameter efficiency, which represents entities by
composing entity-corresponding codewords matched from predefined small-scale
codebooks. We refer to the process of obtaining corresponding codewords of each
entity as entity quantization, for which previous works have designed
complicated strategies. Surprisingly, this paper shows that simple random
entity quantization can achieve similar results to current strategies. We
analyze this phenomenon and reveal that entity codes, the quantization outcomes
for expressing entities, have higher entropy at the code level and Jaccard
distance at the codeword level under random entity quantization. Therefore,
different entities become more easily distinguished, facilitating effective KG
representation. The above results show that current quantization strategies are
not critical for KG representation, and there is still room for improvement in
entity distinguishability beyond current strategies. The code to reproduce our
results is available at https://github.com/JiaangL/RandomQuantization.Comment: Accepted to EMNLP 202
Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks
In this paper, a novel transmissive reconfigurable meta-surface (RMS)
transceiver enabled multi-tier computing network architecture is proposed for
improving computing capability, decreasing computing delay and reducing base
station (BS) deployment cost, in which transmissive RMS equipped with a feed
antenna can be regarded as a new type of multi-antenna system. We formulate a
total energy consumption minimization problem by a joint optimization of
subcarrier allocation, task input bits, time slot allocation, transmit power
allocation and RMS transmissive coefficient while taking into account the
constraints of communication resources and computing resources. This formulated
problem is a non-convex optimization problem due to the high coupling of
optimization variables, which is NP-hard to obtain its optimal solution. To
address the above challenging problems, block coordinate descent (BCD)
technique is employed to decouple the optimization variables to solve the
problem. Specifically, the joint optimization problem of subcarrier allocation,
task input bits, time slot allocation, transmit power allocation and RMS
transmissive coefficient is divided into three subproblems to solve by applying
BCD. Then, the decoupled three subproblems are optimized alternately by using
successive convex approximation (SCA) and difference-convex (DC) programming
until the convergence is achieved. Numerical results verify that our proposed
algorithm is superior in reducing total energy consumption compared to other
benchmarks
Improving Top- N
Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the performance of recommendation algorithms. Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished. In this paper, we propose three schemes to utilize the negative examples in missing data. The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations. Experimental results on two real datasets show that our proposed approaches gain better top-N performance than the baseline ones on both accuracy and diversity
Phonemic Adversarial Attack against Audio Recognition in Real World
Recently, adversarial attacks for audio recognition have attracted much
attention. However, most of the existing studies mainly rely on the
coarse-grain audio features at the instance level to generate adversarial
noises, which leads to expensive generation time costs and weak universal
attacking ability. Motivated by the observations that all audio speech consists
of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT)
paradigm, which attacks the fine-grain audio features at the phoneme level
commonly shared across audio instances, to generate phonemic adversarial
noises, enjoying the more general attacking ability with fast generation speed.
Specifically, for accelerating the generation, a phoneme density balanced
sampling strategy is introduced to sample quantity less but phonemic features
abundant audio instances as the training data via estimating the phoneme
density, which substantially alleviates the heavy dependency on the large
training dataset. Moreover, for promoting universal attacking ability, the
phonemic noise is optimized in an asynchronous way with a sliding window, which
enhances the phoneme diversity and thus well captures the critical fundamental
phonemic patterns. By conducting extensive experiments, we comprehensively
investigate the proposed PAT framework and demonstrate that it outperforms the
SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking
ability improvement)
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