23 accepted papers
Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by…
Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical prediction. However, its progress is hindered by the misalignment between discrete token-level objectives (e.g., cross-entropy) …
Household environments present one of the most common, impactful yet challenging application domains for robotics. Within household scenarios, manipulating deformable objects is particularly difficult, both in simulation and real-world execution, due to varied categories and shapes, complex dynamics…
Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and mu…
Modern Transformers predominantly adopt the Pre-Norm paradigm for its optimization stability, foregoing the superior potential of the unstable Post-Norm architecture. Prior attempts to combine their strengths typically lead to a stability-performance trade-off. We attribute this phenomenon to a stru…
Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain si
The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has driven generalization, progress in offline BBO remains hindered by the lack of unified representations for heterogeneous nume…
Defocus blurring is a prevalent problem in digital pathology imaging, which degrades the quality and accuracy of biomedical research and clinical diagnosis. Existing deblurring methods are mostly based on one-to-one image translation models, which require multiple models to handle different defocus…
The quadratic unconstrained binary optimization (QUBO) is a well-known NP-hard problem that takes an $n\times n$ matrix $Q$ as input and decides an $n$-dimensional 0-1 vector $x$, to optimize a quadratic function. Existing learning-based models that always formulate the solution process as sequentia…
The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice there is a large domain gap between different hospitals' data such as the variable data quality due to differences in acquisition equipment. In addition accurate annotation…
Multi-choice questions (MCQ) are a common method for assessing the world knowledge of large language models (LLMs), demonstrated by benchmarks such as MMLU and C-Eval. However, recent findings indicate that even top-tier LLMs, such as ChatGPT and GPT4, might display inconsistencies when faced with s…
The efficient disassembly of end-of-life electric vehicle batteries(EOL-EVBs) is crucial for green manufacturing and sustainable development. The current pre-programmed disassembly conducted by the Autonomous Mobile Manipulator Robot(AMMR) struggles to meet the disassembly requirements in dynamic en…
Models trained on ultrasound images from one institution typically experience a decline in effectiveness when transferred directly to other institutions. Moreover, unlike natural images, dense and overlapped structures exist in fetus ultrasound images, making the detection of structures more challen…
The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approac…
This work empirically confirms that non-autoregressive translation (NAT) is less robust in decoding batch size and hardware settings than autoregressive translation (AT). To address this issue, we demonstrate that prompting a small number of AT predictions can significantly reduce the performance ga…
In this work, we study the robustness of two typical terminology translation methods: Placeholder (PH) and Code-Switch (CS), concerning (1) the number of constraints and (2) the target constraint length. We identify that existing terminology constraint test sets, such as IATE, Wiktionary, and TICO,…
K-Nearest Neighbor Neural Machine Translation (kNNMT) successfully incorporates external corpus by retrieving word-level representations at test time. Generally, kNNMT borrows the off-the-shelf context representation in the translation task, e.g., the output of the last decoder layer, as the query v…
This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggrega…
Electrocardiogram (ECG) delineation, which provides clinically useful information for the diagnosis of cardiovascular disease, is an essential task in automated ECG analysis. The discrepancies among ECG signals from different datasets, namely domain shifts, may bring severe challenges to the cross-d…
In this paper, we focus on the problem of applying the transformer structure to video captioning effectively. The vanilla transformer is proposed for uni-modal language generation task such as machine translation. However, video captioning is a multimodal learning problem, and the video features hav…
Graph Neural Networks (GNN) are an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise sampling" techniques to reduce training time; However, these metho…
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to t…
Defending adversarial attack is a critical step towards reliable deployment of deep learning empowered solutions for industrial applications. Probabilistic adversarial robustness (PAR), as a theoretical framework, is introduced to neutralize adversarial attacks by concentrating sample probability to…