Data & Analytics

Min-max normalizes each model's predictions to [0,1] before averaging, ensuring equal contribution regardless of score distribution scale.

Sort texts by length before batching with dynamic padding to minimize wasted padding tokens and speed up transformer inference

Applies label smoothing to binary cross-entropy loss to reduce overconfidence and improve generalization in text classification.

Replay keystroke activity logs (Input/Replace/Paste/Remove/Move) against a string buffer to reconstruct the evolving essay text

Dual-head transformer model that jointly predicts start/end span logits from sequence output and answer type from pooled CLS output.

Computes micro F-beta for NER where true positives are determined by Jaccard word-overlap >= threshold rather than exact string match.

Further pretrains a transformer with masked language modeling on the target task's own text before fine-tuning.

Pads each batch to its actual max sequence length instead of the global max_len, reducing wasted computation.

Fine-tunes DeBERTa-v3 for text classification tasks. Use when building text classifiers, sentiment analysis, or multi-label classification on domain-specific data.

Merges multiple training datasets while keeping all positive examples and downsampling negatives to control class imbalance.

Average predictions from each epoch checkpoint with exponentially increasing weights (2^epoch), favoring later more-converged snapshots

Splits long documents into overlapping fixed-length windows with BIO NER tags for BERT token classification on sequences exceeding max length.

Double training data by adding reverse-direction translation pairs with task prefix prompts

Use Adafactor optimizer with label smoothing for seq2seq fine-tuning — memory-efficient and regularizes overconfident predictions

Parse sequential yes/no answers to build inclusion/exclusion sets, then apply compound boolean filters to narrow a candidate list

Ensemble multi-model LLM predictions using weighted combination of average probability, cross-model agreement ratio, and max confidence

Chain a study-level classifier with an image-level detector, merging class probabilities and bounding boxes into a unified prediction

Pre-shard TFRecords into N files each balanced along 3 axes (patient, target, image-count), then KFold over file indices for leak-free triple-stratified folds

Compiles a PyTorch model to a TensorRT FP16 engine via torch_tensorrt for 2-5x inference speedup, saved as reusable TorchScript.

Turn sparse single-frame event labels (impacts, collisions, goals) into a usable detector training set by stamping the positive label onto a ±k-frame window around each event, then dropping any frame that contains no positives — gives the detector enough positive samples without changing the original annotation

Track dice score separately for positive (mask-present) and negative (empty-mask) images to avoid division distortion

Load fluorescence microscopy images stored as 4 separate single-channel PNGs (red microtubules, green target protein, blue nucleus, yellow ER) into a single HxWx4 tensor, preserving the biological semantics of each channel rather than collapsing to RGB

Normalize high-dynamic-range satellite or medical imagery to [0,1] using per-channel percentile clipping to suppress outliers while preserving relative contrast

Aggregate per-slice CNN predictions into a single patient-level injury score by mean-pooling across TTA copies first, then max-pooling across slices — the worst-slice wins, which matches the medical reality that one bad slice is enough to grade the patient