Advances in Neural Information Processing Systems
Overview
Advances in Neural Information Processing Systems is a scientific journal, published since 1989 in English. The journal's country of origin is United States.
Details
Details
Abbr.
Adv Neural Inf Process Syst
Start
1989
End
Continuing
Frequency
Annual
p-ISSN
1049-5258
Country
United States
Language
English
Recent Articles
1.
Cui H, Mao L, Liang X, Zhang J, Ren H, Li Q, et al.
Adv Neural Inf Process Syst
. 2025 Feb;
37:96449-96467.
PMID: 40017809
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these foundation models trained for general usage to specialized domains...
2.
Li Y, Han C, Raghavan V, Mischler G, Mesgarani N
Adv Neural Inf Process Syst
. 2025 Jan;
36:19594-19621.
PMID: 39866554
In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS...
3.
Zhang Z, Sabuncu M
Adv Neural Inf Process Syst
. 2025 Jan;
32():8792-8802.
PMID: 39839708
Deep neural networks (DNNs) have achieved tremendous success in a variety of applications across many disciplines. Yet, their superior performance comes with the expensive cost of requiring correctly annotated large-scale...
4.
Roy S, Wong R, Ni Y
Adv Neural Inf Process Syst
. 2024 Dec;
36:42762-42774.
PMID: 39726833
Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. In this article, we introduce a functional linear structural equation model for causal structure...
5.
Toosi T, Issa E
Adv Neural Inf Process Syst
. 2024 Nov;
37:56979-56997.
PMID: 39553924
In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination....
6.
Gupta S, Zhang Y, Hu X, Prasanna P, Chen C
Adv Neural Inf Process Syst
. 2024 Nov;
36:8186-8207.
PMID: 39484069
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to...
7.
Chee J, Cai Y, Kuleshov V, De Sa C
Adv Neural Inf Process Syst
. 2024 Oct;
36:4396-4429.
PMID: 39416859
This work studies post-training parameter quantization in large language models (LLMs). We introduce quantization with incoherence processing (QuIP), a new method based on the insight that quantization benefits from weight...
8.
Bendel M, Ahmad R, Schniter P
Adv Neural Inf Process Syst
. 2024 Oct;
36:68673-68684.
PMID: 39380744
In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements. Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, super-resolution, inpainting, phase...
9.
Knight P, Duan R
Adv Neural Inf Process Syst
. 2024 Oct;
36:54020-54031.
PMID: 39351341
Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task...
10.
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Andrews B, Ramsey J, Sanchez-Romero R, Camchong J, Kummerfeld E
Adv Neural Inf Process Syst
. 2024 Sep;
36:63945-63956.
PMID: 39280091
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale...