The complexity of human cognition has meant that psychology makes more use of theory and conceptual models than perhaps any other biomedical field. To enable precise quantitative study of the full breadth of phenomena in psychological and psychiatric medicine as well as cognitive aspects of AI safety, there is a need for a mathematical formulation which is both mathematically precise and equally accessible to experts from numerous fields. In this paper we formalize human psychodynamics via the diagrammatic framework of process theory, describe its key properties, and explain the links between a diagrammatic representation and central concepts in analysis of cognitive processes in contexts such as psychotherapy, neurotechnology, AI alignment, AI agent representation of individuals in autonomous negotiations, developing human-like AI systems, and other aspects of AI safety.

Table 3 shows the full results with unlikelihood training and length normalization.COP A H-Swag StoryCloze Winogrande WSC WiC FT 78 .0 PEFT methods we considered and ablate the losses. We use "Question:" and "Answer:" as Since T0 is unable to perform ICL on its own, we also compare to T5+LM, the next-step-prediction language model upon which T0 is based. Due to memory constraints and because of its improved performance, we use ensemble ICL for Table table 10 shows the T-Few ablation results. Per-dataset results of T-Few and the other top-5 methods on RAFT are shown in table 11. 18 # of Param COP A H-Swag StoryCloze WinograndeFull Model Fine-tuning 3B 81 .0

Physics Informed Neural Networks offer a mesh free framework for solving PDEs but are highly sensitive to loss weight selection. We propose two dimensional analysis based weighting schemes, one based on quantifiable terms, and another also incorporating unquantifiable terms for more balanced training. Benchmarks on heat conduction, convection diffusion, and lid driven cavity flows show that the second scheme consistently improves stability and accuracy over equal weighting. Notably, in high Peclet number convection diffusion, where traditional solvers fail, PINNs with our scheme achieve stable, accurate predictions, highlighting their robustness and generalizability in CFD problems.

Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings. However, developing specialist medical VLMs requires substantial computational resources and carefully curated datasets, and it remains unclear under which conditions generalist and specialist medical VLMs each perform best. This study highlights the complementary strengths of specialist medical and generalist VLMs. Specialists remain valuable in modality-aligned use cases, but we find that efficiently fine-tuned generalist VLMs can achieve comparable or even superior performance in most tasks, particularly when transferring to unseen or rare OOD medical modalities. These results suggest that generalist VLMs, rather than being constrained by their lack of specialist medical pretraining, may offer a scalable and cost-effective pathway for advancing clinical AI development.

Clarity: The paper writing is very good, but I find several small problems related to notations, which could make confusion: - Between line 108-109, the authors use both the \bf\xi with a supscript "t" and the \bf\xi without a supscript "t", I guess for the latter the authors mean a general batch of samples does not depend on "t", but it is not explained clearly. Also, sometimes it has "i" in the supscript while othertimes it has "i" in the subscript. However, the reuse of the same notation really makes me confused for a while since it looks like \xi is some element belong to \bf\xi or \bf\xi'. Is this a proof artifact? It makes more sense that if we want to do an averaging here, the w_t's should better have different weights such that the recent updates get higher score.

This paper mainly combines another MOO algorithm and MOO-MTL, and improves the results from last year NIPS paper Multi-objective MTL. The technical contribution for MOO and MTL is limited since this paper just borrow the MOO optimization method directly from reference [24] and reference [29]. Nevertheless, I think this paper has a potential impact in MTL community since I do not find any previous paper achieves similar effects, which could guide people get different high-quality MTL results without random trails. Quality: below the average The quality of the paper is below the bar. Some important part is missing and lack of deep analysis.

Review of "Low-rank matrix reconstruction and clustering" This paper contributes a new algorithm for low-rank matrix reconstruction which is based on an application of Belief Propagation (BP) message-passing to a Bayesian model of the reconstruction problem. The algorithm, as described in the "Supplementary Material", incorporates two simplifying approximations, based on assuming a large number of rows and columns, respectively, in the input matrix. The algorithm is evaluated in a novel manner against Lloyd's K-means algorithm by formulating clustering as a matrix reconstruction problem. It is also compared against Variational Bayes Matrix Factorization (VBMF), which seems to be the only previous message-passing reconstruction algorithm. Cons There are some arguments against accepting the paper.

This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides for the generation and adjustment of Dynamic Movement Primitives (DMP) trajectories for long-horizon task execution. We further create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of our framework on both familiar tasks involving new objects and novel but related tasks, highlighting the potential of LLMs in enhancing robotic system versatility and adaptability. Project website: https://object814.github.io/Task-Condition-With-LLM/

We tackle the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. Compared to neural machine translation on natural language, mathematical formulae have a much smaller vocabulary and much longer sequences of symbols, while their translation requires extreme precision to satisfy mathematical information needs. In this work, we perform the tasks of translating from LaTeX to Mathematica as well as from LaTeX to semantic LaTeX. While recurrent, recursive, and transformer networks struggle with preserving all contained information, we find that convolutional sequence-to-sequence networks achieve 95.1% and 90.7% exact matches, respectively.

Basically, it's just a type of ML algorithm that was built to emulate connections in a brain. It can be used for classification and regression tasks. Today, we're going to go over a classification task. The big thing about NNs is that they are "universal function approximators," meaning they can approximate any function (duh). Compare this with linear regression which ONLY can approximate linear functions. The first layer is called the input layer and has as many neurons as we have features in our data.