23,716 research outputs found
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
Semantic scene completion (SSC) jointly predicts the semantics and geometry
of the entire 3D scene, which plays an essential role in 3D scene understanding
for autonomous driving systems. SSC has achieved rapid progress with the help
of semantic context in segmentation. However, how to effectively exploit the
relationships between the semantic context in semantic segmentation and
geometric structure in scene completion remains under exploration. In this
paper, we propose to solve outdoor SSC from the perspective of representation
separation and BEV fusion. Specifically, we present the network, named SSC-RS,
which uses separate branches with deep supervision to explicitly disentangle
the learning procedure of the semantic and geometric representations. And a BEV
fusion network equipped with the proposed Adaptive Representation Fusion (ARF)
module is presented to aggregate the multi-scale features effectively and
efficiently. Due to the low computational burden and powerful representation
ability, our model has good generality while running in real-time. Extensive
experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art
performance.Comment: 8 pages, 5 figures, IROS202
Defending Black-box Classifiers by Bayesian Boundary Correction
Classifiers based on deep neural networks have been recently challenged by
Adversarial Attack, where the widely existing vulnerability has invoked the
research in defending them from potential threats. Given a vulnerable
classifier, existing defense methods are mostly white-box and often require
re-training the victim under modified loss functions/training regimes. While
the model/data/training specifics of the victim are usually unavailable to the
user, re-training is unappealing, if not impossible for reasons such as limited
computational resources. To this end, we propose a new black-box defense
framework. It can turn any pre-trained classifier into a resilient one with
little knowledge of the model specifics. This is achieved by new joint Bayesian
treatments on the clean data, the adversarial examples and the classifier, for
maximizing their joint probability. It is further equipped with a new
post-train strategy which keeps the victim intact. We name our framework
Bayesian Boundary Correction (BBC). BBC is a general and flexible framework
that can easily adapt to different data types. We instantiate BBC for image
classification and skeleton-based human activity recognition, for both static
and dynamic data. Exhaustive evaluation shows that BBC has superior robustness
and can enhance robustness without severely hurting the clean accuracy,
compared with existing defense methods.Comment: arXiv admin note: text overlap with arXiv:2203.0471
Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision
Large language models (LLMs) have demonstrated remarkable capabilities out of
box for a wide range of applications, yet accuracy still remains a major growth
area, especially in mission-critical domains such as biomedicine. An effective
method to calibrate the confidence level on LLM responses is essential to
automatically detect errors and facilitate human-in-the-loop verification. An
important source of calibration signals stems from expert-stipulated
programmatic supervision, which is often available at low cost but has its own
limitations such as noise and coverage. In this paper, we introduce a Pareto
optimal self-supervision framework that can leverage available programmatic
supervision to systematically calibrate LLM responses by producing a risk score
for every response, without any additional manual efforts. This is accomplished
by learning a harmonizer model to align LLM output with other available
supervision sources, which would assign higher risk scores to more uncertain
LLM responses and facilitate error correction. Experiments on standard relation
extraction tasks in biomedical and general domains demonstrate the promise of
this approach, with our proposed risk scores highly correlated with the real
error rate of LLMs. For the most uncertain test instances, dynamic prompting
based on our proposed risk scores results in significant accuracy improvement
for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA)
weak supervision and GPT-4 results past SOTA supervised results on challenging
evaluation datasets
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202
An advanced deep learning models-based plant disease detection: A review of recent research
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Anticipatory thinking drives our ability to manage risk - identification and
mitigation - in everyday life, from bringing an umbrella when it might rain to
buying car insurance. As AI systems become part of everyday life, they too have
begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and
Go agents have similar capabilities to humans, implicitly managing risks
presented by their opponents. To further increase performance in these tasks,
out-of-distribution evaluation can characterize a model's bias, what we view as
a type of risk management. However, learning to identify and mitigate
low-frequency, high-impact risks is at odds with the observational bias
required to train machine learning models. StarCraft and Go are closed-world
domains whose risks are known and mitigations well documented, ideal for
learning through repetition. Adversarial filtering datasets provide difficult
examples but are laborious to curate and static, both barriers to real-world
risk management. Adversarial robustness focuses on model poisoning under the
assumption there is an adversary with malicious intent, without considering
naturally occurring adversarial examples. These methods are all important steps
towards improving risk management but do so without considering open-worlds. We
unify these open-world risk management challenges with two contributions. The
first is our perception challenges, designed for agents with imperfect
perceptions of their environment whose consequences have a high impact. Our
second contribution are cognition challenges, designed for agents that must
dynamically adjust their risk exposure as they identify new risks and learn new
mitigations. Our goal with these challenges is to spur research into solutions
that assess and improve the anticipatory thinking required by AI agents to
manage risk in open-worlds and ultimately the real-world.Comment: 4 pages, 3 figures, appeared in the non-archival AAAI 2022 Spring
Syposium on "Designing Artificial Intelligence for Open Worlds
Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics
MOTIVATION: Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. AVAILABILITY: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments
Robots operating in real-world environments must reason about possible
outcomes of stochastic actions and make decisions based on partial observations
of the true world state. A major challenge for making accurate and robust
action predictions is the problem of confounding, which if left untreated can
lead to prediction errors. The partially observable Markov decision process
(POMDP) is a widely-used framework to model these stochastic and
partially-observable decision-making problems. However, due to a lack of
explicit causal semantics, POMDP planning methods are prone to confounding bias
and thus in the presence of unobserved confounders may produce underperforming
policies. This paper presents a novel causally-informed extension of "anytime
regularized determinized sparse partially observable tree" (AR-DESPOT), a
modern anytime online POMDP planner, using causal modelling and inference to
eliminate errors caused by unmeasured confounder variables. We further propose
a method to learn offline the partial parameterisation of the causal model for
planning, from ground truth model data. We evaluate our methods on a toy
problem with an unobserved confounder and show that the learned causal model is
highly accurate, while our planning method is more robust to confounding and
produces overall higher performing policies than AR-DESPOT.Comment: 8 pages, 3 figures, submitted to 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation
SimpleMTOD is a simple language model which recasts several sub-tasks in
multimodal task-oriented dialogues as sequence prediction tasks. SimpleMTOD is
built on a large-scale transformer-based auto-regressive architecture, which
has already proven to be successful in uni-modal task-oriented dialogues, and
effectively leverages transfer learning from pre-trained GPT-2. In-order to
capture the semantics of visual scenes, we introduce both local and
de-localized tokens for objects within a scene. De-localized tokens represent
the type of an object rather than the specific object itself and so possess a
consistent meaning across the dataset. SimpleMTOD achieves a state-of-the-art
BLEU score (0.327) in the Response Generation sub-task of the SIMMC 2.0
test-std dataset while performing on par in other multimodal sub-tasks:
Disambiguation, Coreference Resolution, and Dialog State Tracking. This is
despite taking a minimalist approach for extracting visual (and non-visual)
information. In addition the model does not rely on task-specific architectural
changes such as classification heads
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