Model Evaluation

Artificial Intelligence / 24 Sep 2026 / 5 min read

Temperature Scaling Recalibrates Classifier Confidence Without Changing Class Order

A classifier can rank the correct class above every alternative yet attach probabilities that are systematically too concentrated or too diffuse. Temperature scaling addresses that mismatch after training by applying one scalar to the logits before softmax. It changes reported confidence without changing the underlying classifier parameters. The mechanism is narrow. It does not repair incorrect class rankings, add information to the representation, or make every individual probability accurate. Its target is the relationship between confidence and observed outcomes on data representative of deployment.

Artificial Intelligence Calibration

Artificial Intelligence / 23 Sep 2026 / 5 min read

Attention Entropy Measures Concentration, Not Causal Importance

An attention head can place most of its probability mass on one position and still provide little evidence that this position controls the final model output. The entropy of its attention weights captures concentration, not causal influence. That distinction matters when attention maps are inspected as diagnostics. Entropy can reveal whether a head spreads mass broadly or focuses it narrowly for a given query. It cannot, by itself, establish that the highest-weight token carries the feature responsible for a downstream prediction.

Artificial Intelligence Attention

Artificial Intelligence / 17 Sep 2026 / 6 min read

Use Selective Classification to Trade Coverage for Error Rate

A classifier usually returns a label for every input, even when its score distribution is nearly tied or the input sits far from familiar data. Selective classification changes that interface: the system may return a prediction or abstain. The acceptance rule then determines both how many inputs receive predictions and how often those accepted predictions are wrong. This is not the same as making the classifier intrinsically more accurate. Abstention moves some cases out of the automatic-decision set. Its value depends on whether the selection score ranks difficult cases well enough for rejected inputs to contain a disproportionate share of errors.

Artificial Intelligence Classification

Artificial Intelligence / 17 Sep 2026 / 5 min read

Evaluate Probabilistic Classifiers with the Brier Score

Two classifiers can produce the same predicted labels and the same accuracy while assigning very different probabilities to those labels. A system that emits 0.51 for every correct binary decision is not making the same probabilistic claim as one that emits 0.99, even though thresholded accuracy may treat them identically. The Brier score keeps that distinction visible. It measures squared error between predicted probabilities and observed outcomes, so both the selected class and the probability assigned to each outcome affect the result. This makes it useful when downstream code consumes probabilities for ranking, thresholds, abstention, or expected-cost decisions.

Artificial Intelligence Model Evaluation

Artificial Intelligence / 13 Sep 2026 / 6 min read

Detect Out-of-Distribution Inputs with Classifier Energy Scores

A classifier can assign high softmax confidence to an input that does not resemble the data used to fit its parameters. Softmax normalizes scores across the available classes; it does not add a separate class for unfamiliar inputs. As a result, a large maximum probability is not evidence that an input belongs to the expected data distribution. Energy-based out-of-distribution detection uses the full logit vector to produce a scalar score before a deployment policy decides whether an input looks familiar enough to accept. The score is simple to compute for an existing classifier, but its interpretation depends on the model, temperature, data regime, and threshold calibration.

Artificial Intelligence Neural Networks

Artificial Intelligence / 12 Sep 2026 / 9 min read

Stabilize Image Model Predictions with Test-Time Augmentation

Stabilize Image Model Predictions with Test-Time Augmentation An image classifier can give slightly different answers when the same subject is cropped, mirrored, or resized in a way that preserves its meaning. If those transformations are valid for the task, relying on one view leaves useful evidence unused. Test-time augmentation (TTA) runs inference on several valid views of one input and combines their predictions into a final result. TTA is simple to describe, but safe use depends on details that are easy to miss. A transformation must preserve the target, structured outputs may need to be mapped back before aggregation, probability averaging can affect calibration, and every extra view consumes inference capacity.

Artificial Intelligence Computer Vision

Artificial Intelligence / 12 Sep 2026 / 10 min read

Route Uncertain Classifier Predictions with Selective Classification

Route Uncertain Classifier Predictions with Selective Classification A classifier does not have to answer every request. In systems where a bad prediction is costly, forcing a label on every input can be a poor product decision even when the model has strong average accuracy. Selective classification adds a reject option. The system returns a model prediction only when an acceptance rule considers the case suitable; otherwise it abstains and sends the case to a fallback such as human review, a second model, or a request for more information.

Artificial Intelligence Model Evaluation

Artificial Intelligence / 12 Sep 2026 / 8 min read

Reduce Neural Network Inference Cost with Early Exits

Reduce Neural Network Inference Cost with Early Exits A deep neural network normally applies every block to every input, even when an intermediate representation already supports a confident prediction. Early-exit inference changes that fixed-depth path. It attaches prediction heads at intermediate points and lets selected inputs stop before the final block. The appeal is conditional computation: easy cases can consume less compute while ambiguous cases retain access to the full network. The difficult part is deciding when an intermediate prediction is reliable enough to return. A poor exit policy can save computation by silently moving errors toward the shallow heads.

Artificial Intelligence Neural Networks

Artificial Intelligence / 11 Sep 2026 / 11 min read

Let Classifiers Abstain with Selective Prediction

Let Classifiers Abstain with Selective Prediction A classifier does not have to answer every case. In many systems, forcing a prediction on an ambiguous input is worse than sending that input to a human, requesting more information, or falling back to a safer workflow. Selective prediction gives a model that option. The classifier produces its usual prediction, but the system accepts it only when a selection rule considers the case reliable enough. Otherwise, the system abstains.

Artificial Intelligence Machine Learning

Artificial Intelligence / 11 Sep 2026 / 10 min read

Build Prediction Sets with Conformal Prediction

Build Prediction Sets with Conformal Prediction A classifier usually returns one label even when several labels are plausible. That is convenient for software interfaces, but it can hide uncertainty exactly where mistakes are expensive. A document router might be unsure between billing and account, yet an argmax still emits one of them. Conformal prediction offers another interface: return a set of labels sized according to the evidence. Easy inputs can produce one label. Ambiguous inputs can produce several. Under specific assumptions, the procedure also gives a finite-sample coverage guarantee.

Artificial Intelligence Model Evaluation

Artificial Intelligence / 10 Sep 2026 / 10 min read

Whiten Embeddings Without Breaking Vector Search

Whiten Embeddings Without Breaking Vector Search Embedding search can produce a vector space whose dimensions are strongly correlated or whose variance is concentrated in a few directions. When that geometry interferes with retrieval, embedding whitening is one possible post-processing step: center the vectors, rotate them into uncorrelated directions, and rescale those directions to comparable variance. The transformation is simple to describe but easy to misuse. Whitening changes the geometry that your similarity function sees. If you fit it on the wrong data, transform only one side of retrieval, or keep unstable low-variance directions, search quality can get worse even though the transformed covariance looks cleaner.

Artificial Intelligence Embeddings

Artificial Intelligence / 10 Sep 2026 / 11 min read

Improve LLM Fine-Tuning with Rejection Sampling

Improve LLM Fine-Tuning with Rejection Sampling Suppose you can tell a good model response from a bad one, but writing thousands of ideal responses by hand is expensive. A capable language model may already produce acceptable answers some of the time. The problem is that those answers are mixed with weaker ones. Rejection sampling fine-tuning turns that observation into a data-generation loop. For each prompt, generate several candidate responses, evaluate them, keep responses that satisfy a selection rule, and use the accepted prompt-response pairs for supervised fine-tuning. The method can concentrate training on behavior you want without requiring a human to author every target from scratch.

Artificial Intelligence Large Language Models

Artificial Intelligence / 10 Sep 2026 / 10 min read

Evaluate Knowledge Edits Before Trusting Model Updates

Changing one fact in a language model sounds simpler than retraining it. If a product name changes, an organization moves offices, or a fictional knowledge base is updated, knowledge editing aims to change a model’s behavior for that information without running broad training again. The hard part isn’t making one prompt produce the new answer. The hard part is knowing what else changed. A useful evaluation therefore asks more than “did the edit work?” It checks whether the new fact survives reasonable paraphrases, whether unrelated behavior stays stable, and whether the model can use the edited information when another answer depends on it. This article builds that evaluation model and shows how to turn it into a practical test suite.

Artificial Intelligence LLM

Artificial Intelligence / 09 Sep 2026 / 9 min read

Use Predictive Entropy to Detect Uncertain Classifications

Use Predictive Entropy to Detect Uncertain Classifications A classifier can return the same predicted label for two inputs while being much less certain about one of them. If an application only keeps the winning label, that difference disappears. Predictive entropy gives you a compact way to preserve it. It summarizes how spread out a classifier’s predicted probability distribution is: concentrated probability produces low entropy, while probability spread across several classes produces higher entropy.

Artificial Intelligence Machine Learning

Artificial Intelligence / 09 Sep 2026 / 10 min read

Select Active Learning Examples with BALD

When labels are expensive, training on every available example may be impractical. An active learning system tries to spend its labeling budget selectively: train a model on the labels already available, score unlabeled examples, request labels for useful examples, then retrain. A common first idea is to label the examples with the highest predictive entropy. That can help, but entropy mixes together two different reasons for uncertainty. The model may be uncertain because it does not yet know enough, or because the input itself is genuinely ambiguous. More labels are most valuable for the first case.

Artificial Intelligence Active Learning

Artificial Intelligence / 09 Sep 2026 / 12 min read

Evaluate Sequence Models Beyond Training Length

A sequence model can score well on a random test split and still fail when an input is longer than the sequences it saw during training. This matters for language, symbolic reasoning, event sequences, and other tasks where production inputs do not have one fixed length. The problem is easy to hide. If training and test examples come from the same length distribution, an aggregate metric mostly measures performance on familiar lengths. It does not tell you whether the model learned a rule that extends to longer sequences or a strategy that works only inside the observed range.

Artificial Intelligence Machine Learning

Artificial Intelligence / 09 Sep 2026 / 10 min read

Diagnose Embedding Anisotropy Before Tuning Vector Search

Diagnose Embedding Anisotropy Before Tuning Vector Search A vector search system can behave strangely even when its indexing code and similarity calculation are correct. Unrelated items may receive surprisingly high cosine similarities, score differences may look compressed, or many embeddings may point in broadly similar directions. One possible cause is embedding anisotropy: the vectors occupy some directions much more strongly than others instead of being distributed evenly through the representation space. Anisotropy is a property of the embedding geometry, not proof that retrieval is broken. The useful question is whether that geometry is hurting the decisions your system makes.

Artificial Intelligence Embeddings

Artificial Intelligence / 09 Sep 2026 / 8 min read

Contextual Calibration for Few-Shot Classifiers

A language model can behave like a classifier without any parameter updates: give it a few labeled examples, present a new input, and ask it to choose a label. The surprising problem is that the answer can depend not only on the new input, but also on details such as the prompt wording, demonstration order, and label tokens. That creates a practical debugging trap. A prompt may appear to teach the task while also giving the model a baseline preference for one answer before meaningful input is considered.

Artificial Intelligence LLM

Artificial Intelligence / 09 Sep 2026 / 10 min read

Choose Consensus Outputs with Minimum Bayes Risk Decoding

A generative model can assign high probability to an output that is not the most useful answer for your application. This is especially visible when several different outputs are plausible: a translation can have multiple valid phrasings, a summary can emphasize different details, and a structured generator can produce several semantically similar candidates. Greedy decoding chooses locally likely tokens. Beam search searches for a high-probability sequence. Sampling gives you diverse candidates. None of those methods, by itself, asks a different question that is often closer to the application goal: which candidate agrees best with the distribution of plausible outputs?

Artificial Intelligence LLM

Artificial Intelligence / 08 Sep 2026 / 9 min read

Use Test-Time Augmentation Without Hiding Model Errors

A model usually makes one prediction from one representation of an input. That is convenient, but the representation may contain accidental details that should not determine the answer. A product photo can be shifted a few pixels. A scanned digit can be slightly rotated. A crop can place the object closer to one edge than another. Test-time augmentation (TTA) asks the trained model to predict several valid transformations of the same input and then combines those predictions. The technique can make inference less dependent on one particular view, but it also increases compute and can make predictions worse when the transformations change information that matters to the label.

Artificial Intelligence Machine Learning

Artificial Intelligence / 08 Sep 2026 / 8 min read

Estimate LLM Uncertainty with Semantic Entropy

A language model can produce a fluent answer even when it is uncertain. Token probabilities help describe uncertainty during generation, but they can be misleading at the answer level because many different strings can express the same meaning. Consider a question whose correct answer is Paris. A model might generate Paris, The answer is Paris, and France's capital is Paris. These strings differ, yet they represent the same answer. Treating them as three unrelated outcomes exaggerates the apparent uncertainty.

Artificial Intelligence LLM

Artificial Intelligence / 08 Sep 2026 / 9 min read

Detect Out-of-Distribution Inputs with Energy Scores

A classifier can be highly accurate on its test set and still behave confidently on inputs that are unlike anything it was trained to recognize. A product classifier trained on shoes, bags, and watches may receive a photo of a bicycle and still be forced to choose one of its known classes. That creates a deployment problem: ordinary classification answers which known class looks most likely, but many systems also need to ask whether this input resembles the data on which the classifier was validated.

Artificial Intelligence Machine Learning

Artificial Intelligence / 08 Sep 2026 / 8 min read

Deep Ensembles for Model Uncertainty

A neural network can return a confident prediction even when an input is unfamiliar or ambiguous. Looking only at one model’s largest probability can therefore hide an important question: would another plausible model, trained on the same task, make the same decision? A deep ensemble helps answer that question by training several neural networks independently and combining their predictions. The combined prediction can improve robustness in some settings, while disagreement among members provides a practical uncertainty signal. It is not a guarantee that the prediction is correct, and it does not detect every kind of uncertainty.

Artificial Intelligence Machine Learning

Artificial Intelligence / 07 Sep 2026 / 12 min read

Measure Tokenization Efficiency Across Languages

Two prompts can communicate roughly the same amount of information and still consume very different numbers of model tokens. The difference can appear between languages, writing systems, domains, or even formatting styles. That matters because language-model systems usually operate on tokens rather than characters or words. A context window is measured in tokens. Many hosted APIs account for usage in tokens. Longer token sequences can also increase inference work, although the exact latency and compute effect depends on the model, serving stack, batching, caching, and whether the tokens belong to the input or generated output.

Artificial Intelligence LLM