34 research outputs found
Is GPT4 a Good Trader?
Recently, large language models (LLMs), particularly GPT-4, have demonstrated
significant capabilities in various planning and reasoning tasks
\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there
has been a surge of interest among researchers to harness the capabilities of
GPT-4 for the automated design of quantitative factors that do not overlap with
existing factor libraries, with an aspiration to achieve alpha returns
\cite{webpagequant}. In contrast to these work, this study aims to examine the
fidelity of GPT-4's comprehension of classic trading theories and its
proficiency in applying its code interpreter abilities to real-world trading
data analysis. Such an exploration is instrumental in discerning whether the
underlying logic GPT-4 employs for trading is intrinsically reliable.
Furthermore, given the acknowledged interpretative latitude inherent in most
trading theories, we seek to distill more precise methodologies of deploying
these theories from GPT-4's analytical process, potentially offering invaluable
insights to human traders.
To achieve this objective, we selected daily candlestick (K-line) data from
specific periods for certain assets, such as the Shanghai Stock Index. Through
meticulous prompt engineering, we guided GPT-4 to analyze the technical
structures embedded within this data, based on specific theories like the
Elliott Wave Theory. We then subjected its analytical output to manual
evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these
trading theories from multiple dimensions. The results and findings from this
study could pave the way for a synergistic amalgamation of human expertise and
AI-driven insights in the realm of trading
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Recently, deep learning as a service (DLaaS) has emerged as a promising way
to facilitate the employment of deep neural networks (DNNs) for various
purposes. However, using DLaaS also causes potential privacy leakage from both
clients and cloud servers. This privacy issue has fueled the research interests
on the privacy-preserving inference of DNN models in the cloud service. In this
paper, we present a practical solution named BAYHENN for secure DNN inference.
It can protect both the client's privacy and server's privacy at the same time.
The key strategy of our solution is to combine homomorphic encryption and
Bayesian neural networks. Specifically, we use homomorphic encryption to
protect a client's raw data and use Bayesian neural networks to protect the DNN
weights in a cloud server. To verify the effectiveness of our solution, we
conduct experiments on MNIST and a real-life clinical dataset. Our solution
achieves consistent latency decreases on both tasks. In particular, our method
can outperform the best existing method (GAZELLE) by about 5x, in terms of
end-to-end latency.Comment: accepted by IJCAI 2019; camera read
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis of
nephropathy. As the difference between different subcategories is subtle,
doctors often refer to slides from different staining methods to make
decisions. However, creating correspondence across various stains is
labor-intensive, bringing major difficulties in collecting data and training a
vision-based algorithm to assist nephropathy diagnosis. This paper provides an
alternative solution for integrating multi-stained visual cues for glomerulus
classification. Our approach, named generator-to-classifier (G2C), is a
two-stage framework. Given an input image from a specified stain, several
generators are first applied to estimate its appearances in other staining
methods, and a classifier follows to combine visual cues from different stains
for prediction (whether it is pathological, or which type of pathology it has).
We optimize these two stages in a joint manner. To provide a reasonable
initialization, we pre-train the generators in an unlabeled reference set under
an unpaired image-to-image translation task, and then fine-tune them together
with the classifier. We conduct experiments on a glomerulus type classification
dataset collected by ourselves (there are no publicly available datasets for
this purpose). Although joint optimization slightly harms the authenticity of
the generated patches, it boosts classification performance, suggesting more
effective visual cues are extracted in an automatic way. We also transfer our
model to a public dataset for breast cancer classification, and outperform the
state-of-the-arts significantly.Comment: Accepted by AAAI 201
VDC: Versatile Data Cleanser for Detecting Dirty Samples via Visual-Linguistic Inconsistency
The role of data in building AI systems has recently been emphasized by the
emerging concept of data-centric AI. Unfortunately, in the real-world, datasets
may contain dirty samples, such as poisoned samples from backdoor attack, noisy
labels in crowdsourcing, and even hybrids of them. The presence of such dirty
samples makes the DNNs vunerable and unreliable.Hence, it is critical to detect
dirty samples to improve the quality and realiability of dataset. Existing
detectors only focus on detecting poisoned samples or noisy labels, that are
often prone to weak generalization when dealing with dirty samples from other
domains.In this paper, we find a commonality of various dirty samples is
visual-linguistic inconsistency between images and associated labels. To
capture the semantic inconsistency between modalities, we propose versatile
data cleanser (VDC) leveraging the surpassing capabilities of multimodal large
language models (MLLM) in cross-modal alignment and reasoning.It consists of
three consecutive modules: the visual question generation module to generate
insightful questions about the image; the visual question answering module to
acquire the semantics of the visual content by answering the questions with
MLLM; followed by the visual answer evaluation module to evaluate the
inconsistency.Extensive experiments demonstrate its superior performance and
generalization to various categories and types of dirty samples.Comment: 22 pages,5 figures,17 table
Semantic Equivariant Mixup
Mixup is a well-established data augmentation technique, which can extend the
training distribution and regularize the neural networks by creating ''mixed''
samples based on the label-equivariance assumption, i.e., a proportional mixup
of the input data results in the corresponding labels being mixed in the same
proportion. However, previous mixup variants may fail to exploit the
label-independent information in mixed samples during training, which usually
contains richer semantic information. To further release the power of mixup, we
first improve the previous label-equivariance assumption by the
semantic-equivariance assumption, which states that the proportional mixup of
the input data should lead to the corresponding representation being mixed in
the same proportion. Then a generic mixup regularization at the representation
level is proposed, which can further regularize the model with the semantic
information in mixed samples. At a high level, the proposed semantic
equivariant mixup (sem) encourages the structure of the input data to be
preserved in the representation space, i.e., the change of input will result in
the obtained representation information changing in the same way. Different
from previous mixup variants, which tend to over-focus on the label-related
information, the proposed method aims to preserve richer semantic information
in the input with semantic-equivariance assumption, thereby improving the
robustness of the model against distribution shifts. We conduct extensive
empirical studies and qualitative analyzes to demonstrate the effectiveness of
our proposed method. The code of the manuscript is in the supplement.Comment: Under revie