2 citations · 3 across the 11 of their papers we have counts for
14 papers
Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend
Junichiro Niimi
Apple Silicon machines with large unified memory make it possible to hold large tensors on a desktop GPU. However, we found that PyTorch's Metal Performance Shaders (MPS) backend s…
Cross-Block Conditioning in Deep Boltzmann Machines for Statistical Data Fusion
Junichiro Niimi
Statistical data fusion combines two panels that share a block of covariates but observe disjoint outcome blocks, and in its traditional form no row observes both outcomes at once.…
Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary
Junichiro Niimi
Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parame…
Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention
Junichiro Niimi
Marketing decisions reflect the interaction of latent consumer heterogeneity, time-varying internal states, and explicit interventions, a structure that current prediction- and lan…
Distortion Instead of Hallucination: The Effect of Reasoning Under Strict Constraints
Junichiro Niimi
With the widespread adoption of large language models (LLMs), hallucinations, which are non-factual fabrications in model outputs, have become serious concerns. Reasoning capabilit…
The Mouth is Not the Brain: Bridging Energy-Based World Models and Language Generation
Junichiro Niimi
Large Language Models (LLMs) generate fluent text, yet whether they truly understand the world or merely produce plausible texts about it remains contested. We propose an architect…
Hierarchical Memorization in Large Language Models: Evidence from Citation Generation
Junichiro Niimi
Large language models (LLMs) generate fluent text across a wide range of tasks, but the fabrication of non-existent academic citations remains a critical and well-documented failur…
Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy
Junichiro Niimi
Large language models (LLMs) have been increasingly applied to a wide range of tasks, from natural language understanding to code generation. While they have also been used to assi…
Stable LLM Ensemble: Interaction between Example Representativeness and Diversity
Junichiro Niimi
Large language models (LLMs) have achieved remarkable results in wide range of domains. However, the accuracy and robustness of one-shot LLM predictions remain highly sensitive to…
Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Junichiro Niimi
Large language models (LLMs) are now widely used across many fields, including marketing research. Sentiment analysis, in particular, helps firms understand consumer preferences. W…
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
Junichiro Niimi
With the advance of large language models (LLMs), LLMs have been utilized for the various tasks. However, the issues of variability and reproducibility of results from each trial o…
Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation
Junichiro Niimi
User-generated contents (UGCs) on online platforms allow marketing researchers to understand consumer preferences for products and services. With the advance of large language mode…
An Efficient Multimodal Learning Framework to Comprehend Consumer Preferences Using BERT and Cross-Attention
Junichiro Niimi
Today, the acquisition of various behavioral log data has enabled deeper understanding of customer preferences and future behaviors in the marketing field. In particular, multimoda…
Multimodal Deep Learning of Word-of-Mouth Text and Demographics to Predict Customer Rating: Handling Consumer Heterogeneity in Marketing
Junichiro Niimi
In the marketing field, understanding consumer heterogeneity, which is the internal or psychological difference among consumers that cannot be captured by behavioral logs, has long…