13 accepted papers
Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data distributions and sensitivity characteristics inherent in LIC models. This leads to a suboptimal trade-off between perform
Extracting a target source from underdetermined mixtures is challenging for beamforming approaches. Recently proposed time-frequency-bin-wise switching (TFS) and linear combination (TFLC) strategies mitigate this by combining multiple beamformers in each time-frequency (TF) bin and choosing combinat…
Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible to error accumulation. To address this, we propose PartDiffuser, a novel semi-autoregressive diffusion framework for poin
Large language models (LLMs) have demonstrated impressive performance in machine translation, but still struggle with unseen low-resource languages, especially those written in underrepresented scripts. To investigate whether LLMs can translate such languages with the help of linguistic resources, w
Numerous adversarial defense methods have been proposed to strengthen the robustness of Natural Language Processing (NLP) models against adversarial attacks. However, many of these methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is of…
Stereophonic music source separation (MSS) is a problem of extracting individual source tracks, e.g. bass, drums, vocals, from a stereo music recording. Deep neural network (DNN) based MSS systems have demonstrated great promise though spatial panning cues and time-frequency spectral structures in s…
Natural Language Processing (NLP) models have gained great success on clean texts, but they are known to be vulnerable to adversarial examples typically crafted by synonym substitutions. In this paper, we target to solve this problem and find that word embedding is important to the certified robustn…
Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the perform…
We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to inconsistent outputs, and we propose a novel robust training method, termed \textit{Fast Triplet Metric Learning (FTML)}…
Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification. For text classification, however, existing synonym substitution based adversarial attacks are effective but not very efficient to be incorporated into practi…
In the area of natural language processing, deep learning models are recently known to be vulnerable to various types of adversarial perturbations, but relatively few works are done on the defense side. Especially, there exists few effective defense method against the successful synonym substitution…
A key challenge for reinforcement learning is solving long-horizon planning problems. Recent work has leveraged programs to guide reinforcement learning in these settings. However, these approaches impose a high manual burden on the user since they must provide a guiding program for every new task.…
We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication. We quantify the amount of communication as the maximum degree of the communication graph; this metric captures settings where agents have…